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Moerbeek M. Optimal design of cluster randomized crossover trials with a continuous outcome: Optimal number of time periods and treatment switches under a fixed number of clusters or fixed budget. Behav Res Methods 2024:10.3758/s13428-024-02505-1. [PMID: 39271634 DOI: 10.3758/s13428-024-02505-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2024] [Indexed: 09/15/2024]
Abstract
In the cluster randomized crossover trial, a sequence of treatment conditions, rather than just one treatment condition, is assigned to each cluster. This contribution studies the optimal number of time periods in studies with a treatment switch at the end of each time period, and the optimal number of treatment switches in a trial with a fixed number of time periods. This is done for trials with a fixed number of clusters, and for trials in which the costs per cluster, subject, and treatment switch are taken into account using a budgetary constraint. The focus is on trials with a cross-sectional design where a continuous outcome variable is measured at the end of each time period. An exponential decay correlation structure is used to model dependencies among subjects within the same cluster. A linear multilevel mixed model is used to estimate the treatment effect and its associated variance. The optimal design minimizes this variance. Matrix algebra is used to identify the optimal design and other highly efficient designs. For a fixed number of clusters, a design with the maximum number of time periods is optimal and treatment switches should occur at each time period. However, when a budgetary constraint is taken into account, the optimal design may have fewer time periods and fewer treatment switches. The Shiny app was developed to facilitate the use of the methodology in this contribution.
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Affiliation(s)
- Mirjam Moerbeek
- Department of Methodology and Statistics, Utrecht University, PO Box 80140, Utrecht, TC, 3508, The Netherlands.
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2
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Ma R, Kiyasseh D, Laca JA, Kocielnik R, Wong EY, Chu TN, Cen S, Yang CH, Dalieh IS, Haque TF, Goldenberg MG, Huang X, Anandkumar A, Hung AJ. Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study. J Endourol 2024; 38:884-891. [PMID: 37905524 DOI: 10.1089/end.2023.0328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023] Open
Abstract
Introduction: Automated skills assessment can provide surgical trainees with objective, personalized feedback during training. Here, we measure the efficacy of artificial intelligence (AI)-based feedback on a robotic suturing task. Materials and Methods: Forty-two participants with no robotic surgical experience were randomized to a control or feedback group and video-recorded while completing two rounds (R1 and R2) of suturing tasks on a da Vinci surgical robot. Participants were assessed on needle handling and needle driving, and feedback was provided via a visual interface after R1. For feedback group, participants were informed of their AI-based skill assessment and presented with specific video clips from R1. For control group, participants were presented with randomly selected video clips from R1 as a placebo. Participants from each group were further labeled as underperformers or innate-performers based on a median split of their technical skill scores from R1. Results: Demographic features were similar between the control (n = 20) and feedback group (n = 22) (p > 0.05). Observing the improvement from R1 to R2, the feedback group had a significantly larger improvement in needle handling score (0.30 vs -0.02, p = 0.018) when compared with the control group, although the improvement of needle driving score was not significant when compared with the control group (0.17 vs -0.40, p = 0.074). All innate-performers exhibited similar improvements across rounds, regardless of feedback (p > 0.05). In contrast, underperformers in the feedback group improved more than the control group in needle handling (p = 0.02). Conclusion: AI-based feedback facilitates surgical trainees' acquisition of robotic technical skills, especially underperformers. Future research will extend AI-based feedback to additional suturing skills, surgical tasks, and experience groups.
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Affiliation(s)
- Runzhuo Ma
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Dani Kiyasseh
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA
| | - Jasper A Laca
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Rafal Kocielnik
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA
| | - Elyssa Y Wong
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Timothy N Chu
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Steven Cen
- Radiology Department, University of Southern California, Los Angeles, California, USA
| | - Cherine H Yang
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Istabraq S Dalieh
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Taseen F Haque
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Mitch G Goldenberg
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - Xiuzhen Huang
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
- Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Anima Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA
| | - Andrew J Hung
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
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3
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Harrall KK, Sauder KA, Glueck DH, Shenkman EA, Muller KE. Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024; 25:433-445. [PMID: 38767783 PMCID: PMC11239604 DOI: 10.1007/s11121-024-01673-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2024] [Indexed: 05/22/2024]
Abstract
We give examples of three features in the design of randomized controlled clinical trials which can increase power and thus decrease sample size and costs. We consider an example multilevel trial with several levels of clustering. For a fixed number of independent sampling units, we show that power can vary widely with the choice of the level of randomization. We demonstrate that power and interpretability can improve by testing a multivariate outcome rather than an unweighted composite outcome. Finally, we show that using a pooled analytic approach, which analyzes data for all subgroups in a single model, improves power for testing the intervention effect compared to a stratified analysis, which analyzes data for each subgroup in a separate model. The power results are computed for a proposed prevention research study. The trial plans to randomize adults to either telehealth (intervention) or in-person treatment (control) to reduce cardiovascular risk factors. The trial outcomes will be measures of the Essential Eight, a set of scores for cardiovascular health developed by the American Heart Association which can be combined into a single composite score. The proposed trial is a multilevel study, with outcomes measured on participants, participants treated by the same provider, providers nested within clinics, and clinics nested within hospitals. Investigators suspect that the intervention effect will be greater in rural participants, who live farther from clinics than urban participants. The results use published, exact analytic methods for power calculations with continuous outcomes. We provide example code for power analyses using validated software.
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Affiliation(s)
- Kylie K Harrall
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Gainesville, 32606, FL, USA.
| | - Katherine A Sauder
- Department of Implementation Science, Wake Forest University School of Medicine, 475 Vine Street, Winston-Salem, 27101, NC, USA
| | - Deborah H Glueck
- Department of Pediatrics, University of Colorado School of Medicine, 13123 E. 16th Ave., Aurora, 80045, CO, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Gainesville, 32606, FL, USA
| | - Keith E Muller
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Gainesville, 32606, FL, USA
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4
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Wang X, Chen X, Goldfeld KS, Taljaard M, Li F. Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs. Stat Methods Med Res 2024; 33:1115-1136. [PMID: 38689556 PMCID: PMC11347095 DOI: 10.1177/09622802241247736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel-arm cluster randomized design. While statistical methods have been developed for designing cluster randomized crossover trials, they have exclusively focused on testing the overall average treatment effect, with little attention to differential treatment effects across subpopulations. Recently, interest has grown in understanding whether treatment effects may vary across pre-specified patient subpopulations, such as those defined by demographic or clinical characteristics. In this article, we consider the two-treatment two-period cluster randomized crossover design under either a cross-sectional or closed-cohort sampling scheme, where it is of interest to detect the heterogeneity of treatment effect via an interaction test. Assuming a patterned correlation structure for both the covariate and the outcome, we derive new sample size formulas for testing the heterogeneity of treatment effect with continuous outcomes based on linear mixed models. Our formulas also address unequal cluster sizes and therefore allow us to analytically assess the impact of unequal cluster sizes on the power of the interaction test in cluster randomized crossover designs. We conduct simulations to confirm the accuracy of the proposed methods, and illustrate their application in two real cluster randomized crossover trials.
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Affiliation(s)
- Xueqi Wang
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Xinyuan Chen
- Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS, USA
| | - Keith S Goldfeld
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA
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5
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Liu J, Li F. Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials. Stat Methods Med Res 2024:9622802241247717. [PMID: 38813761 DOI: 10.1177/09622802241247717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Cluster randomized crossover and stepped wedge cluster randomized trials are two types of longitudinal cluster randomized trials that leverage both the within- and between-cluster comparisons to estimate the treatment effect and are increasingly used in healthcare delivery and implementation science research. While the variance expressions of estimated treatment effect have been previously developed from the method of generalized estimating equations for analyzing cluster randomized crossover trials and stepped wedge cluster randomized trials, little guidance has been provided for optimal designs to ensure maximum efficiency. Here, an optimal design refers to the combination of optimal cluster-period size and optimal number of clusters that provide the smallest variance of the treatment effect estimator or maximum efficiency under a fixed total budget. In this work, we develop optimal designs for multiple-period cluster randomized crossover trials and stepped wedge cluster randomized trials with continuous outcomes, including both closed-cohort and repeated cross-sectional sampling schemes. Local optimal design algorithms are proposed when the correlation parameters in the working correlation structure are known. MaxiMin optimal design algorithms are proposed when the exact values are unavailable, but investigators may specify a range of correlation values. The closed-form formulae of local optimal design and MaxiMin optimal design are derived for multiple-period cluster randomized crossover trials, where the cluster-period size and number of clusters are decimal. The decimal estimates from closed-form formulae can then be used to investigate the performances of integer estimates from local optimal design and MaxiMin optimal design algorithms. One unique contribution from this work, compared to the previous optimal design research, is that we adopt constrained optimization techniques to obtain integer estimates under the MaxiMin optimal design. To assist practical implementation, we also develop four SAS macros to find local optimal designs and MaxiMin optimal designs.
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Affiliation(s)
- Jingxia Liu
- Division of Public Health Sciences, Department of Surgery and Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Fan Li
- Department of Biostatistics, Yale University, New Haven, CT, USA
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6
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Wang X, Turner EL, Li F. Designing individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations. Stat Med 2024; 43:358-378. [PMID: 38009329 PMCID: PMC10939061 DOI: 10.1002/sim.9966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/04/2023] [Accepted: 11/08/2023] [Indexed: 11/28/2023]
Abstract
Individually randomized group treatment (IRGT) trials, in which the clustering of outcome is induced by group-based treatment delivery, are increasingly popular in public health research. IRGT trials frequently incorporate longitudinal measurements, of which the proper sample size calculations should account for correlation structures reflecting both the treatment-induced clustering and repeated outcome measurements. Given the relatively sparse literature on designing longitudinal IRGT trials, we propose sample size procedures for continuous and binary outcomes based on the generalized estimating equations approach, employing the block exchangeable correlation structures with different correlation parameters for the treatment arm and for the control arm, and surveying five marginal mean models with different assumptions of time effect: no-time constant treatment effect, linear-time constant treatment effect, categorical-time constant treatment effect, linear time by treatment interaction, and categorical time by treatment interaction. Closed-form sample size formulas are derived for continuous outcomes, which depends on the eigenvalues of the correlation matrices; detailed numerical sample size procedures are proposed for binary outcomes. Through simulations, we demonstrate that the empirical power agrees well with the predicted power, for as few as eight groups formed in the treatment arm, when data are analyzed using the matrix-adjusted estimating equations for the correlation parameters with a bias-corrected sandwich variance estimator.
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Affiliation(s)
- Xueqi Wang
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06511, USA
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA
- Duke Global Health Institute, Duke University, Durham, NC, 27710, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06511, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, CT, 06511, USA
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7
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Tong G, Tong J, Li F. Designing multicenter individually randomized group treatment trials. Biom J 2024; 66:e2200307. [PMID: 37768850 DOI: 10.1002/bimj.202200307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/29/2023] [Accepted: 07/23/2023] [Indexed: 09/30/2023]
Abstract
In an individually randomized group treatment (IRGT) trial, participant outcomes can be positively correlated due to, for example, shared therapists in treatment delivery. Oftentimes, because of limited treatment resources or participants at one location, an IRGT trial can be carried out across multiple centers. This design can be subject to potential correlations in the participant outcomes between arms within the same center. While the design of a single-center IRGT trial has been studied, little is known about the planning of a multicenter IRGT trial. To address this gap, this paper provides analytical sample size formulas for designing multicenter IRGT trials with a continuous endpoint under the linear mixed model framework. We found that accounting for the additional center-level correlation at the design stage can lead to sample size reduction, and the magnitude of reduction depends on the amount of between-therapist correlation. However, if the variance components of therapist-level random effects are considered as input parameters in the design stage, accounting for the additional center-level variance component has no impact on the sample size estimation. We presented our findings through numeric illustrations and performed simulation studies to validate our sample size procedures under different scenarios. Optimal design configurations under the multicenter IRGT trials have also been discussed, and two real-world trial examples are drawn to illustrate the use of our method.
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Affiliation(s)
- Guangyu Tong
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Jiaqi Tong
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
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Li MY, Kwok SWH, Tan JYB, Bressington D, Liu XL, Wang T, Chen SL. Somatic acupressure for the fatigue-sleep disturbance-depression symptom cluster in breast cancer survivors: A phase II randomized controlled trial. Eur J Oncol Nurs 2023; 66:102380. [PMID: 37607468 DOI: 10.1016/j.ejon.2023.102380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/08/2023] [Accepted: 06/29/2023] [Indexed: 08/24/2023]
Abstract
OBJECTIVE To evaluate the feasibility of the somatic acupressure (SA) for managing the fatigue-sleep disturbance-depression symptom cluster (FSDSC) among breast cancer (BC) survivors and its preliminary effects. METHODS In this Phase II randomized controlled trial (RCT), 51 participants were randomised evenly into the true SA group, sham SA group, and usual care group. All the participants received usual care. The two SA groups performed additional true or sham self-acupressure daily for seven weeks. The primary outcomes related to the assessment of participants' recruitment and compliance with study questionnaires and interventions. Clinical outcomes assessed the preliminary effects of SA on fatigue, sleep disturbance, depression, and quality of life. Semi-structured interviews were undertaken to capture participants' experiences of participating in this study. The statistical effects of the intervention on the outcomes were modelled in repeated measures ANOVA and adjusted generalized estimating equations. RESULTS Forty-five participants completed the SA intervention. No adverse events were reported. Over 85% of the participants could sustain for 25 days or more and 15 min or more per session, but the adherence to the intervention requirement was yet to improve. The group by time effect of the FSDSC and depression were significant (p < 0.05). Qualitative findings showed that participants positively viewed SA as a beneficial strategy for symptom management. CONCLUSIONS The SA intervention protocol and the trial procedures were feasible. The results demonstrated signs of improvements in targeted outcomes, and a full-scale RCT is warranted to validate the effects of SA on the FSDSC.
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Affiliation(s)
- Meng-Yuan Li
- School of Nursing, Faculty of Health, Charles Darwin University, Brisbane Centre, 410 Ann Street, Brisbane, QLD, Australia.
| | - Stephen Wai Hang Kwok
- Murdoch University, Harry Butler Institute, Perth, WA, Australia; School of Nursing, Faculty of Health, Charles Darwin University, Ellengowan Drive, Darwin, NT, Australia.
| | - Jing-Yu Benjamin Tan
- School of Nursing, Faculty of Health, Charles Darwin University, Ellengowan Drive, Darwin, NT, Australia.
| | - Daniel Bressington
- School of Nursing, Faculty of Health, Charles Darwin University, Ellengowan Drive, Darwin, NT, Australia.
| | - Xian-Liang Liu
- School of Nursing, Faculty of Health, Charles Darwin University, Brisbane Centre, 410 Ann Street, Brisbane, QLD, Australia.
| | - Tao Wang
- School of Nursing, Faculty of Health, Charles Darwin University, Brisbane Centre, 410 Ann Street, Brisbane, QLD, Australia.
| | - Shun-Li Chen
- The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, Sichuan, 646000, PR China.
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Li F, Kasza J, Turner EL, Rathouz PJ, Forbes AB, Preisser JS. Generalizing the information content for stepped wedge designs: A marginal modeling approach. Scand Stat Theory Appl 2023; 50:1048-1067. [PMID: 37601275 PMCID: PMC10434823 DOI: 10.1111/sjos.12615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 09/02/2022] [Indexed: 11/30/2022]
Abstract
Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll-out. While a complete design requires clusters to collect data in all periods, resource and patient-centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance-stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
| | - Paul J. Rathouz
- Department of Population Health, The University of Texas at Austin, Austin, Texas, USA
| | - Andrew B. Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - John S. Preisser
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Raymond BL, Allen BFS, Freundlich RE, Parrish CG, Jayaram JE, Wanderer JP, Rice TW, Lindsell CJ, Scharfman KH, Dear ML, Gao Y, Hiser WD, McEvoy MD. The IMpact of PerioperAtive KeTamine on Enhanced Recovery after Abdominal Surgery (IMPAKT ERAS): protocol for a pragmatic, randomized, double-blinded, placebo-controlled trial. BMC Anesthesiol 2023; 23:227. [PMID: 37391729 PMCID: PMC10311882 DOI: 10.1186/s12871-023-02177-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/12/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND Multimodal analgesic strategies that reduce perioperative opioid consumption are well-supported in Enhanced Recovery After Surgery (ERAS) literature. However, the optimal analgesic regimen has not been established, as the contributions of each individual agent to the overall analgesic efficacy with opioid reduction remains unknown. Perioperative ketamine infusions can decrease opioid consumption and opioid-related side effects. However, as opioid requirements are drastically minimized within ERAS models, the differential effects of ketamine within an ERAS pathway remain unknown. We aim to pragmatically investigate through a learning healthcare system infrastructure how the addition of a perioperative ketamine infusion to mature ERAS pathways affects functional recovery. METHODS The IMPAKT ERAS trial (IMpact of PerioperAtive KeTamine on Enhanced Recovery after Abdominal Surgery) is a single center, pragmatic, randomized, blinded, placebo-controlled trial. 1544 patients undergoing major abdominal surgery will be randomly allocated to receive intraoperative and postoperative (up to 48 h) ketamine versus placebo infusions as part of a perioperative multimodal analgesic regimen. The primary outcome is length of stay, defined as surgical start time until hospital discharge. Secondary outcomes will include a variety of in-hospital clinical end points derived from the electronic health record. DISCUSSION We aimed to launch a large-scale, pragmatic trial that would easily integrate into routine clinical workflow. Implementation of a modified consent process was critical to preserving our pragmatic design, permitting an efficient, low-cost model without reliance on external study personnel. Therefore, we partnered with leaders of our Investigational Review Board to develop a novel, modified consent process and shortened written consent form that would meet all standard elements of informed consent, yet also allow clinical providers the ability to recruit and enroll patients during their clinical workflow. Our trial design has created a platform for subsequent pragmatic studies at our institution. TRIAL REGISTRATION NCT04625283, Pre-results.
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Affiliation(s)
- Britany L Raymond
- Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Drive 4648 TVC, Nashville, TN, 37232, USA.
| | - Brian F S Allen
- Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Drive 4648 TVC, Nashville, TN, 37232, USA
| | - Robert E Freundlich
- Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Drive 4648 TVC, Nashville, TN, 37232, USA
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, 2525 West End Ave Ste. 600, Nashville, TN, 37203, USA
| | - Crystal G Parrish
- Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Drive 4648 TVC, Nashville, TN, 37232, USA
| | - Jennifer E Jayaram
- Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Drive 4648 TVC, Nashville, TN, 37232, USA
| | - Jonathan P Wanderer
- Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Drive 4648 TVC, Nashville, TN, 37232, USA
| | - Todd W Rice
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, 2525 West End Ave Ste. 600, Nashville, TN, 37203, USA
| | - Christopher J Lindsell
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, 2525 West End Ave Ste. 600, Nashville, TN, 37203, USA
| | - Kevin H Scharfman
- Department of Pharmaceutical Services, Vanderbilt University Medical Center, 1211 Medical Center Drive B-101, Nashville, TN, 37232, USA
| | - Mary L Dear
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, 2525 West End Ave Ste. 600, Nashville, TN, 37203, USA
| | - Yue Gao
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, 2525 West End Ave Ste. 600, Nashville, TN, 37203, USA
| | - William D Hiser
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, 2525 West End Ave Ste. 600, Nashville, TN, 37203, USA
| | - Matthew D McEvoy
- Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Drive 4648 TVC, Nashville, TN, 37232, USA
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11
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Davis-Plourde K, Taljaard M, Li F. Sample size considerations for stepped wedge designs with subclusters. Biometrics 2023; 79:98-112. [PMID: 34719017 PMCID: PMC9054939 DOI: 10.1111/biom.13596] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
The stepped wedge cluster randomized trial (SW-CRT) is an increasingly popular design for evaluating health service delivery or policy interventions. An essential consideration of this design is the need to account for both within-period and between-period correlations in sample size calculations. Especially when embedded in health care delivery systems, many SW-CRTs may have subclusters nested in clusters, within which outcomes are collected longitudinally. However, existing sample size methods that account for between-period correlations have not allowed for multiple levels of clustering. We present computationally efficient sample size procedures that properly differentiate within-period and between-period intracluster correlation coefficients in SW-CRTs in the presence of subclusters. We introduce an extended block exchangeable correlation matrix to characterize the complex dependencies of outcomes within clusters. For Gaussian outcomes, we derive a closed-form sample size expression that depends on the correlation structure only through two eigenvalues of the extended block exchangeable correlation structure. For non-Gaussian outcomes, we present a generic sample size algorithm based on linearization and elucidate simplifications under canonical link functions. For example, we show that the approximate sample size formula under a logistic linear mixed model depends on three eigenvalues of the extended block exchangeable correlation matrix. We provide an extension to accommodate unequal cluster sizes and validate the proposed methods via simulations. Finally, we illustrate our methods in two real SW-CRTs with subclusters.
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Affiliation(s)
- Kendra Davis-Plourde
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Heath, University of Ottawa, Ottawa, Ontario, Canada
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
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12
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Cruz NA, Melo OO, Martinez CA. A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures. Stat Pap (Berl) 2023. [DOI: 10.1007/s00362-022-01391-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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13
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Zhang Y, Preisser JS, Turner EL, Rathouz PJ, Toles M, Li F. A general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials. Stat Methods Med Res 2023; 32:71-87. [PMID: 36253078 DOI: 10.1177/09622802221129861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Stepped wedge designs have uni-directional crossovers at randomly assigned time points (steps) where clusters switch from control to intervention condition. Incomplete stepped wedge designs are increasingly used in cluster randomized trials of health care interventions and have periods without data collection due to logistical, resource and patient-centered considerations. The development of sample size formulae for stepped wedge trials has primarily focused on complete designs and continuous responses. Addressing this gap, a general, fast, non-simulation based power procedure is proposed for generalized estimating equations analysis of complete and incomplete stepped wedge designs and its predicted power is compared to simulated power for binary and continuous responses. An extensive set of simulations for six and twelve clusters is based upon the Connect-Home trial with an incomplete stepped wedge design. Results show that empirical test size is well controlled using a t-test with bias-corrected sandwich variance estimator for as few as six clusters. Analytical power agrees well with a simulated power in scenarios with twelve clusters. For six clusters, analytical power is similar to simulated power with estimation using the correctly specified model-based variance estimator. To explore the impact of study design choice on power, the proposed fast GEE power method is applied to the Connect-Home trial design, four alternative incomplete stepped wedge designs and one complete design.
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Affiliation(s)
- Ying Zhang
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - John S Preisser
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Paul J Rathouz
- Department of Population Health, The University of Texas at Austin, Austin, TX, USA
| | - Mark Toles
- School of Nursing, University of North Carolina, Chapel Hill, NC, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA
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14
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Gallis JA, Wang X, Rathouz PJ, Preisser JS, Li F, Turner EL. power swgee: GEE-based power calculations in stepped wedge cluster randomized trials. THE STATA JOURNAL 2022; 22:811-841. [PMID: 36968149 PMCID: PMC10035664 DOI: 10.1177/1536867x221140953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Stepped wedge cluster randomized trials are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal and crossover nature of a SW-CRT, complex analysis techniques are often needed which makes appropriately powering SW-CRTs challenging. In this paper, we introduce a newly-developed SW-CRT power calculator, embedded within the power command in Stata. The power calculator assumes a marginal model (i.e., generalized estimating equations [GEE]) for the primary analysis of SW-CRTs, for which other currently available SW-CRT power calculators may not be suitable. The program accommodates complete cross-sectional and closed-cohort designs, and includes multilevel correlation structures appropriate for such designs. We discuss the methods and formulae underlying our SW-CRT calculator, and provide illustrative examples of the use of power swgee. We provide suggestions about the choice of parameters in power swgee, and conclude by discussing areas of future research which may improve the program.
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Affiliation(s)
- John A Gallis
- Department of Biostatistics, Duke University, Duke Global Health Institute, Durham, NC
| | - Xueqi Wang
- Department of Biostatistics, Duke University, Duke Global Health Institute, Durham, NC
| | - Paul J Rathouz
- Department of Population Health, University of Texas at Austin, Dell Medical School, Austin, TX
| | - John S Preisser
- Department of Biosttistics, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, NC
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, Center for Methods in Implementation, Prevention Science, New Haven, CT
| | - Elizabeth L Turner
- Department of Biostatistics, Duke University, Duke Global Health Institute, Durham, NC
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15
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Blaha O, Esserman D, Li F. Design and analysis of cluster randomized trials with time-to-event outcomes under the additive hazards mixed model. Stat Med 2022; 41:4860-4885. [PMID: 35908796 PMCID: PMC9588628 DOI: 10.1002/sim.9541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 05/04/2022] [Accepted: 07/19/2022] [Indexed: 11/12/2022]
Abstract
A primary focus of current methods for cluster randomized trials (CRTs) has been for continuous, binary, and count outcomes, with relatively less attention given to right-censored, time-to-event outcomes. In this article, we detail considerations for sample size requirement and statistical inference in CRTs with time-to-event outcomes when the intervention effect parameter is specified through the additive hazards mixed model (AHMM), which includes a frailty term to explicitly account for the dependency between the failure times. First, we discuss improved inference for the treatment effect parameter via bias-corrected sandwich variance estimators and randomization-based test under AHMM, addressing potential small-sample biases in CRTs. Next, we derive a new sample size formula for AHMM analysis of CRTs accommodating both equal and unequal cluster sizes. When the cluster sizes vary, our sample size formula depends on the mean and coefficient of variation of cluster sizes, based on which we articulate the impact of cluster size variation in CRTs with time-to-event outcomes. Furthermore, we obtain the insight that the classical variance inflation factor for CRTs with a non-censored outcome can in fact apply to CRTs with a time-to-event outcome, providing that an appropriate definition of the intraclass correlation coefficient is considered under AHMM. Simulation studies are carried out to illustrate key design and analysis considerations in CRTs with a small to moderate number of clusters. The proposed sample size procedure and analytical methods are further illustrated using the context of the STrategies to Reduce Injuries and Develop Confidence in Elders CRT.
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Affiliation(s)
- Ondrej Blaha
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Denise Esserman
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, Connecticut, USA
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16
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Gettel CJ, Yiadom MYA, Bernstein SL, Grudzen CR, Nath B, Li F, Hwang U, Hess EP, Melnick ER. Pragmatic clinical trial design in emergency medicine: Study considerations and design types. Acad Emerg Med 2022; 29:1247-1257. [PMID: 35475533 PMCID: PMC9790188 DOI: 10.1111/acem.14513] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 04/04/2022] [Accepted: 04/25/2022] [Indexed: 01/25/2023]
Abstract
Pragmatic clinical trials (PCTs) focus on correlation between treatment and outcomes in real-world clinical practice, yet a guide highlighting key study considerations and design types for emergency medicine investigators pursuing this important study type is not available. Investigators conducting emergency department (ED)-based PCTs face multiple decisions within the planning phase to ensure robust and meaningful study findings. The PRagmatic Explanatory Continuum Indicator Summary 2 (PRECIS-2) tool allows trialists to consider both pragmatic and explanatory components across nine domains, shaping the trial design to the purpose intended by the investigators. Aside from the PRECIS-2 tool domains, ED-based investigators conducting PCTs should also consider randomization techniques, human subjects concerns, and integration of trial components within the electronic health record. The authors additionally highlight the advantages, disadvantages, and rationale for the use of four common randomized study design types to be considered in PCTs: parallel, crossover, factorial, and stepped-wedge. With increasing emphasis on the conduct of PCTs, emergency medicine investigators will benefit from a rigorous approach to clinical trial design.
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Affiliation(s)
- Cameron J. Gettel
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, CT, USA
| | - Maame Yaa A.B. Yiadom
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Corita R. Grudzen
- Ronald O. Perelman Department of Emergency Medicine and Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Bidisha Nath
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ula Hwang
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
- Geriatrics Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Erik P. Hess
- Department of Emergency Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Edward R. Melnick
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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17
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Bauer M, Glenn T, Achtyes ED, Alda M, Agaoglu E, Altınbaş K, Andreassen OA, Angelopoulos E, Ardau R, Aydin M, Ayhan Y, Baethge C, Bauer R, Baune BT, Balaban C, Becerra-Palars C, Behere AP, Behere PB, Belete H, Belete T, Belizario GO, Bellivier F, Belmaker RH, Benedetti F, Berk M, Bersudsky Y, Bicakci Ş, Birabwa-Oketcho H, Bjella TD, Brady C, Cabrera J, Cappucciati M, Castro AMP, Chen WL, Cheung EYW, Chiesa S, Crowe M, Cuomo A, Dallaspezia S, Del Zompo M, Desai P, Dodd S, Etain B, Fagiolini A, Fellendorf FT, Ferensztajn-Rochowiak E, Fiedorowicz JG, Fountoulakis KN, Frye MA, Geoffroy PA, Gonzalez-Pinto A, Gottlieb JF, Grof P, Haarman BCM, Harima H, Hasse-Sousa M, Henry C, Høffding L, Houenou J, Imbesi M, Isometsä ET, Ivkovic M, Janno S, Johnsen S, Kapczinski F, Karakatsoulis GN, Kardell M, Kessing LV, Kim SJ, König B, Kot TL, Koval M, Kunz M, Lafer B, Landén M, Larsen ER, Lenger M, Lewitzka U, Licht RW, Lopez-Jaramillo C, MacKenzie A, Madsen HØ, Madsen SAKA, Mahadevan J, Mahardika A, Manchia M, Marsh W, Martinez-Cengotitabengoa M, Martiny K, Mashima Y, McLoughlin DM, Meesters Y, Melle I, Meza-Urzúa F, Mok YM, Monteith S, Moorthy M, Morken G, Mosca E, Mozzhegorov AA, Munoz R, Mythri SV, Nacef F, Nadella RK, Nakanotani T, Nielsen RE, O'Donovan C, Omrani A, Osher Y, Ouali U, Pantovic-Stefanovic M, Pariwatcharakul P, Petite J, Pfennig A, Ruiz YP, Pinna M, Pompili M, Porter R, Quiroz D, Rabelo-da-Ponte FD, Ramesar R, Rasgon N, Ratta-Apha W, Ratzenhofer M, Redahan M, Reddy MS, Reif A, Reininghaus EZ, Richards JG, Ritter P, Rybakowski JK, Sathyaputri L, Scippa ÂM, Simhandl C, Smith D, Smith J, Stackhouse PW, Stein DJ, Stilwell K, Strejilevich S, Su KP, Subramaniam M, Sulaiman AH, Suominen K, Tanra AJ, Tatebayashi Y, Teh WL, Tondo L, Torrent C, Tuinstra D, Uchida T, Vaaler AE, Vieta E, Viswanath B, Yoldi-Negrete M, Yalcinkaya OK, Young AH, Zgueb Y, Whybrow PC. Association between polarity of first episode and solar insolation in bipolar I disorder. J Psychosom Res 2022; 160:110982. [PMID: 35932492 PMCID: PMC7615104 DOI: 10.1016/j.jpsychores.2022.110982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/14/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Circadian rhythm disruption is commonly observed in bipolar disorder (BD). Daylight is the most powerful signal to entrain the human circadian clock system. This exploratory study investigated if solar insolation at the onset location was associated with the polarity of the first episode of BD I. Solar insolation is the amount of electromagnetic energy from the Sun striking a surface area of the Earth. METHODS Data from 7488 patients with BD I were collected at 75 sites in 42 countries. The first episode occurred at 591 onset locations in 67 countries at a wide range of latitudes in both hemispheres. Solar insolation values were obtained for every onset location, and the ratio of the minimum mean monthly insolation to the maximum mean monthly insolation was calculated. This ratio is largest near the equator (with little change in solar insolation over the year), and smallest near the poles (where winter insolation is very small compared to summer insolation). This ratio also applies to tropical locations which may have a cloudy wet and clear dry season, rather than winter and summer. RESULTS The larger the change in solar insolation throughout the year (smaller the ratio between the minimum monthly and maximum monthly values), the greater the likelihood the first episode polarity was depression. Other associated variables were being female and increasing percentage of gross domestic product spent on country health expenditures. (All coefficients: P ≤ 0.001). CONCLUSION Increased awareness and research into circadian dysfunction throughout the course of BD is warranted.
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Affiliation(s)
- Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - Eric D Achtyes
- Michigan State University College of Human Medicine, Division of Psychiatry & Behavioral Medicine, Grand Rapids, MI, USA; Pine Rest Christian Mental Health Services, Grand Rapids, MI, USA
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Esen Agaoglu
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Kürşat Altınbaş
- Department of Psychiatry, Selcuk University Faculty of Medicine, Mazhar Osman Mood Center, Konya, Turkey
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Elias Angelopoulos
- Department of Psychiatry, National and Capodistrian University of Athens, Medical School, Eginition Hospital, Athens, Greece
| | - Raffaella Ardau
- Section of Neurosciences and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Sardinia, Italy
| | - Memduha Aydin
- Department of Psychiatry, Selcuk University Faculty of Medicine, Konya, Turkey
| | - Yavuz Ayhan
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Christopher Baethge
- Department of Psychiatry and Psychotherapy, University of Cologne Medical School, Cologne, Germany
| | - Rita Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Ceylan Balaban
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Johann Wolfgang Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | | | - Aniruddh P Behere
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI, USA
| | - Prakash B Behere
- Department of Psychiatry, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences (Deemed University), Wardha, India
| | - Habte Belete
- Department of Psychiatry, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Tilahun Belete
- Department of Psychiatry, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Gabriel Okawa Belizario
- Bipolar Disorder Research Program, Department of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil
| | - Frank Bellivier
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris, INSERM UMR-S1144, Université de Paris, FondaMental Foundation, Paris, France
| | - Robert H Belmaker
- Professor Emeritus of Psychiatry, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Francesco Benedetti
- University Vita-Salute San Raffaele, Milan, Italy; Psychiatry & Clinical Psychobiology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia
| | - Yuly Bersudsky
- Department of Psychiatry, Faculty of Health Sciences, Beer Sheva Mental Health Center, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Şule Bicakci
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey; Department of Psychiatry, Baskent University Faculty of Medicine, Ankara, Turkey
| | | | - Thomas D Bjella
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Conan Brady
- Department of Psychiatry, Trinity College Dublin, St Patrick's University Hospital, Dublin, Ireland
| | - Jorge Cabrera
- Mood Disorders Clinic, Dr. Jose Horwitz Psychiatric Institute, Santiago de Chile, Chile
| | | | - Angela Marianne Paredes Castro
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
| | - Wei-Ling Chen
- Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, Chiayi, Taiwan
| | | | - Silvia Chiesa
- Department of Mental Health and Substance Abuse, Piacenza, Italy
| | - Marie Crowe
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Alessandro Cuomo
- Department of Molecular Medicine, University of Siena School of Medicine, Siena, Italy
| | - Sara Dallaspezia
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Maria Del Zompo
- Section of Neurosciences and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Sardinia, Italy
| | | | - Seetal Dodd
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Department of Psychiatry, University of Melbourne, Parkville, Victoria, Australia
| | - Bruno Etain
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris, INSERM UMR-S1144, Université de Paris, FondaMental Foundation, Paris, France
| | - Andrea Fagiolini
- Department of Molecular Medicine, University of Siena School of Medicine, Siena, Italy
| | - Frederike T Fellendorf
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | | | - Jess G Fiedorowicz
- Department of Psychiatry, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Kostas N Fountoulakis
- 3rd Department of Psychiatry, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Mark A Frye
- Department of Psychiatry & Psychology, Mayo Clinic Depression Center, Mayo Clinic, Rochester, MN, USA
| | - Pierre A Geoffroy
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hopital Bichat - Claude Bernard, F-75018 Paris, France; GHU Paris - Psychiatry & Neurosciences, 1 rue Cabanis, 75014 Paris, France; Université de Paris, NeuroDiderot, Inserm, FHU I2-D2, F-75019 Paris, France
| | - Ana Gonzalez-Pinto
- BIOARABA. Department of Psychiatry, University Hospital of Alava, University of the Basque Country, CIBERSAM, Vitoria, Spain
| | - John F Gottlieb
- Department of Psychiatry, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Paul Grof
- Mood Disorders Center of Ottawa and the Department of Psychiatry, University of Toronto, Canada
| | - Bartholomeus C M Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Hirohiko Harima
- Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital, Setagaya, Tokyo, Japan
| | - Mathias Hasse-Sousa
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Chantal Henry
- Department of Psychiatry, GHU Paris Psychiatrie & Neurosciences, F-75014, Paris France, Université de Paris, F-75006 Paris, France
| | - Lone Høffding
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Josselin Houenou
- Université Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, APHP, Mondor Univ Hospitals, Fondation FondaMental, F-94010 Créteil, France; Université Paris Saclay, CEA, Neurospin, F-91191 Gif-sur-Yvette, France
| | | | - Erkki T Isometsä
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; National Institute for Health and Welfare, Helsinki, Finland
| | - Maja Ivkovic
- University Clinical Center of Serbia, Clinic for Psychiatry, Belgrade, Serbia
| | - Sven Janno
- Department of Psychiatry, University of Tartu, Tartu, Estonia
| | - Simon Johnsen
- Unit for Psychiatric Research, Aalborg University Hospital, Aalborg, Denmark
| | - Flávio Kapczinski
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Gregory N Karakatsoulis
- 3rd Department of Psychiatry, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Mathias Kardell
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Seong Jae Kim
- Department of Psychiatry, Chosun University School of Medicine, Gwangju, Republic of Korea
| | - Barbara König
- BIPOLAR Zentrum Wiener Neustadt, Wiener Neustadt, Austria
| | - Timur L Kot
- Khanty-Mansiysk Clinical Psychoneurological Hospital, Khanty-Mansiysk, Russia
| | - Michael Koval
- Department of Neuroscience, Michigan State University, East Lansing, MI, USA
| | - Mauricio Kunz
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Beny Lafer
- Bipolar Disorder Research Program, Department of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil
| | - Mikael Landén
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik R Larsen
- Mental Health Department Odense, University Clinic and Department of Regional Health Research, University of Southern Denmark, Esbjerg, Denmark
| | - Melanie Lenger
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | - Ute Lewitzka
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Rasmus W Licht
- Psychiatry - Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Carlos Lopez-Jaramillo
- Mood Disorders Program, Hospital Universitario San Vicente Fundación, Research Group in Psychiatry, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia
| | - Alan MacKenzie
- Forensic Psychiatry, University of Glasgow, NHS Greater Glasgow and Clyde, Glasgow, UK
| | | | | | - Jayant Mahadevan
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Agustine Mahardika
- Department of Psychiatry, Faculty of Medicine, Mataram University, Mataram, Indonesia
| | - Mirko Manchia
- Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada; Section of Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Wendy Marsh
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA
| | - Monica Martinez-Cengotitabengoa
- Osakidetza, Basque Health Service, BioAraba Health Research Institute, University of the Basque Country, Spain; The Psychology Clinic of East Anglia, Norwich, United Kingdom
| | - Klaus Martiny
- Copenhagen University Hospitals, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Yuki Mashima
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Declan M McLoughlin
- Dept of Psychiatry & Trinity College Institute of Neuroscience, Trinity College Dublin, St Patrick's University Hospital, Dublin, Ireland
| | - Ybe Meesters
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Ingrid Melle
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Fátima Meza-Urzúa
- Department of Child and Adolescent Psychiatry und Psychotherapy, SHG Klinikum, Idar-Oberstein, Germany
| | - Yee Ming Mok
- Department of Mood and Anxiety disorders, Institute of Mental Health, Singapore City, Singapore
| | - Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA
| | - Muthukumaran Moorthy
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Gunnar Morken
- Department of Mental Health, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; Department of Psychiatry, St Olavs' University Hospital, Trondheim, Norway
| | - Enrica Mosca
- Section of Neurosciences and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Sardinia, Italy
| | | | - Rodrigo Munoz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Starlin V Mythri
- Makunda Christian Leprosy and General Hospital, Bazaricherra, Assam 788727, India
| | - Fethi Nacef
- Razi Hospital, Faculty of Medicine, University of Tunis-El Manar, Tunis, Tunisia
| | - Ravi K Nadella
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Takako Nakanotani
- Affective Disorders Research Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo, Japan
| | - René Ernst Nielsen
- Psychiatry - Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Claire O'Donovan
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Adel Omrani
- Tunisian Bipolar Forum, Érable Médical Cabinet 324, Lac 2, Tunis, Tunisia
| | - Yamima Osher
- Department of Psychiatry, Faculty of Health Sciences, Beer Sheva Mental Health Center, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Uta Ouali
- Razi Hospital, Faculty of Medicine, University of Tunis-El Manar, Tunis, Tunisia
| | | | - Pornjira Pariwatcharakul
- Department of Psychiatry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Joanne Petite
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | | | - Marco Pinna
- Section of Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy; Lucio Bini Mood Disorder Center, Cagliari, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health and Sensory Organs, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Richard Porter
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Danilo Quiroz
- Deparment of Psychiatry, Diego Portales University, Santiago de Chile, Chile
| | | | - Raj Ramesar
- SA MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, South Africa
| | - Natalie Rasgon
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Palo Alto, CA, USA
| | - Woraphat Ratta-Apha
- Department of Psychiatry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Michaela Ratzenhofer
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | - Maria Redahan
- Department of Psychiatry, Trinity College Dublin, St Patrick's University Hospital, Dublin, Ireland
| | - M S Reddy
- Asha Bipolar Clinic, Asha Hospital, Hyderabad, Telangana, India
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Johann Wolfgang Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | - Jenny Gringer Richards
- Departments of Psychiatry, Epidemiology, and Internal Medicine, Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Janusz K Rybakowski
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Leela Sathyaputri
- Departments of Psychiatry, Epidemiology, and Internal Medicine, Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA
| | - Ângela M Scippa
- Department of Neuroscience and Mental Health, Federal University of Bahia, Salvador, Brazil
| | - Christian Simhandl
- Bipolar Zentrum Wiener Neustadt, Sigmund Freud Privat Universität, Vienna, Austria
| | - Daniel Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, UK
| | - José Smith
- AREA, Assistance and Research in Affective Disorders, Buenos Aires, Argentina
| | - Paul W Stackhouse
- Science Directorate/Climate Science Branch, NASA Langley Research Center, Hampton, VA, USA
| | - Dan J Stein
- Department of Psychiatry, MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa
| | - Kellen Stilwell
- Pine Rest Christian Mental Health Services, Grand Rapids, MI, USA
| | - Sergio Strejilevich
- AREA, Assistance and Research in Affective Disorders, Buenos Aires, Argentina
| | - Kuan-Pin Su
- College of Medicine, China Medical University (CMU), Taichung, Taiwan; An-Nan Hospital, China Medical University, Tainan, Taiwan
| | | | - Ahmad Hatim Sulaiman
- Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kirsi Suominen
- Department of Social Services and Health Care, Psychiatry, City of Helsinki, Helsinki, Finland
| | - Andi J Tanra
- Department of Psychiatry, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
| | - Yoshitaka Tatebayashi
- Affective Disorders Research Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo, Japan
| | - Wen Lin Teh
- Research Division, Institute of Mental Health, Singapore
| | - Leonardo Tondo
- McLean Hospital-Harvard Medical School, Boston, MA, USA; Mood Disorder Lucio Bini Centers, Cagliari e Roma, Italy
| | - Carla Torrent
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Daniel Tuinstra
- Pine Rest Christian Mental Health Services, Grand Rapids, MI, USA
| | - Takahito Uchida
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia
| | - Arne E Vaaler
- Department of Mental Health, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; Department of Psychiatry, St Olavs' University Hospital, Trondheim, Norway
| | - Eduard Vieta
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Biju Viswanath
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Maria Yoldi-Negrete
- Subdirección de Investigaciones Clínicas. Instituto Nacional de Psiquiatría Ramón de la Fuente Muñíz, Mexico City, Mexico
| | - Oguz Kaan Yalcinkaya
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Allan H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yosra Zgueb
- Razi Hospital, Faculty of Medicine, University of Tunis-El Manar, Tunis, Tunisia
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
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18
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Li F, Lu W, Wang Y, Pan Z, Greene EJ, Meng G, Meng C, Blaha O, Zhao Y, Peduzzi P, Esserman D. A comparison of analytical strategies for cluster randomized trials with survival outcomes in the presence of competing risks. Stat Methods Med Res 2022; 31:1224-1241. [PMID: 35290139 PMCID: PMC10518064 DOI: 10.1177/09622802221085080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
While statistical methods for analyzing cluster randomized trials with continuous and binary outcomes have been extensively studied and compared, little comparative evidence has been provided for analyzing cluster randomized trials with survival outcomes in the presence of competing risks. Motivated by the Strategies to Reduce Injuries and Develop Confidence in Elders trial, we carried out a simulation study to compare the operating characteristics of several existing population-averaged survival models, including the marginal Cox, marginal Fine and Gray, and marginal multi-state models. For each model, we found that adjusting for the intraclass correlations through the sandwich variance estimator effectively maintained the type I error rate when the number of clusters is large. With no more than 30 clusters, however, the sandwich variance estimator can exhibit notable negative bias, and a permutation test provides better control of type I error inflation. Under the alternative, the power for each model is differentially affected by two types of intraclass correlations-the within-individual and between-individual correlations. Furthermore, the marginal Fine and Gray model occasionally leads to higher power than the marginal Cox model or the marginal multi-state model, especially when the competing event rate is high. Finally, we provide an illustrative analysis of Strategies to Reduce Injuries and Develop Confidence in Elders trial using each analytical strategy considered.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, CT, USA
| | - Wenhan Lu
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Yuxuan Wang
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Zehua Pan
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Erich J Greene
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, New Haven, CT, USA
| | - Guanqun Meng
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Can Meng
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, New Haven, CT, USA
| | - Ondrej Blaha
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, New Haven, CT, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, New Haven, CT, USA
| | - Peter Peduzzi
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, New Haven, CT, USA
| | - Denise Esserman
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, New Haven, CT, USA
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19
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Kasza J, Bowden R, Hooper R, Forbes AB. The batched stepped wedge design: A design robust to delays in cluster recruitment. Stat Med 2022; 41:3627-3641. [PMID: 35596691 PMCID: PMC9541502 DOI: 10.1002/sim.9438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 04/13/2022] [Accepted: 05/05/2022] [Indexed: 11/08/2022]
Abstract
Stepped wedge designs are an increasingly popular variant of longitudinal cluster randomized trial designs, and roll out interventions across clusters in a randomized, but step-wise fashion. In the standard stepped wedge design, assumptions regarding the effect of time on outcomes may require that all clusters start and end trial participation at the same time. This would require ethics approvals and data collection procedures to be in place in all clusters before a stepped wedge trial can start in any cluster. Hence, although stepped wedge designs are useful for testing the impacts of many cluster-based interventions on outcomes, there can be lengthy delays before a trial can commence. In this article, we introduce "batched" stepped wedge designs. Batched stepped wedge designs allow clusters to commence the study in batches, instead of all at once, allowing for staggered cluster recruitment. Like the stepped wedge, the batched stepped wedge rolls out the intervention to all clusters in a randomized and step-wise fashion: a series of self-contained stepped wedge designs. Provided that separate period effects are included for each batch, software for standard stepped wedge sample size calculations can be used. With this time parameterization, in many situations including when linear models are assumed, sample size calculations reduce to the setting of a single stepped wedge design with multiple clusters per sequence. In these situations, sample size calculations will not depend on the delays between the commencement of batches. Hence, the power of batched stepped wedge designs is robust to unexpected delays between batches.
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Affiliation(s)
- Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rhys Bowden
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Richard Hooper
- Centre for Primary Care and Public Health, Queen Mary University of London, London, UK
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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20
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Wang X, Turner EL, Preisser JS, Li F. Power considerations for generalized estimating equations analyses of four-level cluster randomized trials. Biom J 2022; 64:663-680. [PMID: 34897793 PMCID: PMC9574475 DOI: 10.1002/bimj.202100081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 09/01/2021] [Accepted: 09/06/2021] [Indexed: 01/10/2023]
Abstract
In this article, we develop methods for sample size and power calculations in four-level intervention studies when intervention assignment is carried out at any level, with a particular focus on cluster randomized trials (CRTs). CRTs involving four levels are becoming popular in healthcare research, where the effects are measured, for example, from evaluations (level 1) within participants (level 2) in divisions (level 3) that are nested in clusters (level 4). In such multilevel CRTs, we consider three types of intraclass correlations between different evaluations to account for such clustering: that of the same participant, that of different participants from the same division, and that of different participants from different divisions in the same cluster. Assuming arbitrary link and variance functions, with the proposed correlation structure as the true correlation structure, closed-form sample size formulas for randomization carried out at any level (including individually randomized trials within a four-level clustered structure) are derived based on the generalized estimating equations approach using the model-based variance and using the sandwich variance with an independence working correlation matrix. We demonstrate that empirical power corresponds well with that predicted by the proposed method for as few as eight clusters, when data are analyzed using the matrix-adjusted estimating equations for the correlation parameters with a bias-corrected sandwich variance estimator, under both balanced and unbalanced designs.
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Affiliation(s)
- Xueqi Wang
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27707, USA
- Duke Global Health Institute, Durham, NC, 27707, USA
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27707, USA
- Duke Global Health Institute, Durham, NC, 27707, USA
| | - John S. Preisser
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, 06511, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, CT, 06511, USA
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21
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Chen X, Li F. Finite-sample adjustments in variance estimators for clustered competing risks regression. Stat Med 2022; 41:2645-2664. [PMID: 35288959 DOI: 10.1002/sim.9375] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/23/2022] [Accepted: 02/23/2022] [Indexed: 12/19/2022]
Abstract
The marginal Fine-Gray proportional subdistribution hazards model is a popular approach to directly study the association between covariates and the cumulative incidence function with clustered competing risks data, which often arise in multicenter randomized trials or multilevel observational studies. To account for the within-cluster correlations between failure times, the uncertainty of the regression parameters estimators is quantified by the robust sandwich variance estimator, which may have unsatisfactory performance with a limited number of clusters. To overcome this limitation, we propose four bias-corrected variance estimators to reduce the negative bias of the usual sandwich variance estimator, extending the bias-correction techniques from generalized estimating equations with noncensored exponential family outcomes to clustered competing risks outcomes. We further compare their finite-sample operating characteristics through simulations and two real data examples. In particular, we found the Mancl and DeRouen (MD) type sandwich variance estimator generally has the smallest bias. Furthermore, with a small number of clusters, the Wald t -confidence interval with the MD sandwich variance estimator carries close to nominal coverage for the cluster-level effect parameter. The t -confidence intervals based on the sandwich variance estimator with any one of the three types of multiplicative bias correction or the z -confidence interval with the Morel, Bokossa and Neerchal (MBN) type sandwich variance estimator have close to nominal coverage for the individual-level effect parameter. Finally, we develop a user-friendly R package crrcbcv implementing the proposed sandwich variance estimators to assist practical applications.
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Affiliation(s)
- Xinyuan Chen
- Department of Mathematics and Statistics, Mississippi State University, Starkville, Mississippi, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.,Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, Connecticut, USA.,Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
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22
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Tian Z, Preisser JS, Esserman D, Turner EL, Rathouz PJ, Li F. Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes. Biom J 2021; 64:419-439. [PMID: 34596912 PMCID: PMC9292617 DOI: 10.1002/bimj.202100112] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/15/2021] [Accepted: 08/07/2021] [Indexed: 12/31/2022]
Abstract
The stepped wedge (SW) design is a type of unidirectional crossover design where cluster units switch from control to intervention condition at different prespecified time points. While a convention in study planning is to assume the cluster‐period sizes are identical, SW cluster randomized trials (SW‐CRTs) involving repeated cross‐sectional designs frequently have unequal cluster‐period sizes, which can impact the efficiency of the treatment effect estimator. In this paper, we provide a comprehensive investigation of the efficiency impact of unequal cluster sizes for generalized estimating equation analyses of SW‐CRTs, with a focus on binary outcomes as in the Washington State Expedited Partner Therapy trial. Several major distinctions between our work and existing work include the following: (i) we consider multilevel correlation structures in marginal models with binary outcomes; (ii) we study the implications of both the between‐cluster and within‐cluster imbalances in sizes; and (iii) we provide a comparison between the independence working correlation versus the true working correlation and detail the consequences of ignoring correlation estimation in SW‐CRTs with unequal cluster sizes. We conclude that the working independence assumption can lead to substantial efficiency loss and a large sample size regardless of cluster‐period size variability in SW‐CRTs, and recommend accounting for correlations in the analysis. To improve study planning, we additionally provide a computationally efficient search algorithm to estimate the sample size in SW‐CRTs accounting for unequal cluster‐period sizes, and conclude by illustrating the proposed approach in the context of the Washington State study.
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Affiliation(s)
- Zibo Tian
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - John S Preisser
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Denise Esserman
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.,Yale Center for Analytical Sciences, New Haven, CT, USA
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.,Duke Global Health Institute, Durham, NC, USA
| | - Paul J Rathouz
- Department of Population Health, The University of Texas at Austin, Austin, TX, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.,Yale Center for Analytical Sciences, New Haven, CT, USA.,Center for Methods in Implementation and Prevention Science, Yale University, New Haven, CT, USA
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23
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Bauer M, Glenn T, Achtyes ED, Alda M, Agaoglu E, Altınbaş K, Andreassen OA, Angelopoulos E, Ardau R, Vares EA, Aydin M, Ayhan Y, Baethge C, Bauer R, Baune BT, Balaban C, Becerra-Palars C, Behere AP, Behere PB, Belete H, Belete T, Belizario GO, Bellivier F, Belmaker RH, Benedetti F, Berk M, Bersudsky Y, Bicakci Ş, Birabwa-Oketcho H, Bjella TD, Brady C, Cabrera J, Cappucciati M, Castro AMP, Chen WL, Cheung EYW, Chiesa S, Crowe M, Cuomo A, Dallaspezia S, Del Zompo M, Desai P, Dodd S, Donix M, Etain B, Fagiolini A, Fellendorf FT, Ferensztajn-Rochowiak E, Fiedorowicz JG, Fountoulakis KN, Frye MA, Geoffroy PA, Gonzalez-Pinto A, Gottlieb JF, Grof P, Haarman BCM, Harima H, Hasse-Sousa M, Henry C, Høffding L, Houenou J, Imbesi M, Isometsä ET, Ivkovic M, Janno S, Johnsen S, Kapczinski F, Karakatsoulis GN, Kardell M, Kessing LV, Kim SJ, König B, Kot TL, Koval M, Kunz M, Lafer B, Landén M, Larsen ER, Lenger M, Lewitzka U, Licht RW, Lopez-Jaramillo C, MacKenzie A, Madsen HØ, Madsen SAKA, Mahadevan J, Mahardika A, Manchia M, Marsh W, Martinez-Cengotitabengoa M, Martiny K, Mashima Y, McLoughlin DM, Meesters Y, Melle I, Meza-Urzúa F, Ming MY, Monteith S, Moorthy M, Morken G, Mosca E, Mozzhegorov AA, Munoz R, Mythri SV, Nacef F, Nadella RK, Nakanotani T, Nielsen RE, O'Donovan C, Omrani A, Osher Y, Ouali U, Pantovic-Stefanovic M, Pariwatcharakul P, Petite J, Pfennig A, Ruiz YP, Pilhatsch M, Pinna M, Pompili M, Porter R, Quiroz D, Rabelo-da-Ponte FD, Ramesar R, Rasgon N, Ratta-Apha W, Ratzenhofer M, Redahan M, Reddy MS, Reif A, Reininghaus EZ, Richards JG, Ritter P, Rybakowski JK, Sathyaputri L, Scippa ÂM, Simhandl C, Severus E, Smith D, Smith J, Stackhouse PW, Stein DJ, Stilwell K, Strejilevich S, Su KP, Subramaniam M, Sulaiman AH, Suominen K, Tanra AJ, Tatebayashi Y, Teh WL, Tondo L, Torrent C, Tuinstra D, Uchida T, Vaaler AE, Veeh J, Vieta E, Viswanath B, Yoldi-Negrete M, Yalcinkaya OK, Young AH, Zgueb Y, Whybrow PC. Variations in seasonal solar insolation are associated with a history of suicide attempts in bipolar I disorder. Int J Bipolar Disord 2021; 9:26. [PMID: 34467430 PMCID: PMC8408297 DOI: 10.1186/s40345-021-00231-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/13/2021] [Indexed: 01/09/2023] Open
Abstract
Background Bipolar disorder is associated with circadian disruption and a high risk of suicidal behavior. In a previous exploratory study of patients with bipolar I disorder, we found that a history of suicide attempts was associated with differences between winter and summer levels of solar insolation. The purpose of this study was to confirm this finding using international data from 42% more collection sites and 25% more countries. Methods Data analyzed were from 71 prior and new collection sites in 40 countries at a wide range of latitudes. The analysis included 4876 patients with bipolar I disorder, 45% more data than previously analyzed. Of the patients, 1496 (30.7%) had a history of suicide attempt. Solar insolation data, the amount of the sun’s electromagnetic energy striking the surface of the earth, was obtained for each onset location (479 locations in 64 countries). Results This analysis confirmed the results of the exploratory study with the same best model and slightly better statistical significance. There was a significant inverse association between a history of suicide attempts and the ratio of mean winter insolation to mean summer insolation (mean winter insolation/mean summer insolation). This ratio is largest near the equator which has little change in solar insolation over the year, and smallest near the poles where the winter insolation is very small compared to the summer insolation. Other variables in the model associated with an increased risk of suicide attempts were a history of alcohol or substance abuse, female gender, and younger birth cohort. The winter/summer insolation ratio was also replaced with the ratio of minimum mean monthly insolation to the maximum mean monthly insolation to accommodate insolation patterns in the tropics, and nearly identical results were found. All estimated coefficients were significant at p < 0.01. Conclusion A large change in solar insolation, both between winter and summer and between the minimum and maximum monthly values, may increase the risk of suicide attempts in bipolar I disorder. With frequent circadian rhythm dysfunction and suicidal behavior in bipolar disorder, greater understanding of the optimal roles of daylight and electric lighting in circadian entrainment is needed.
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Affiliation(s)
- Michael Bauer
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - Eric D Achtyes
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Esen Agaoglu
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Kürşat Altınbaş
- Department of Psychiatry, Selcuk University Faculty of Medicine, Mazhar Osman Mood Center, Konya, Turkey
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Elias Angelopoulos
- Department of Psychiatry, National and Capodistrian University of Athens, Medical School, Eginition Hospital, Athens, Greece
| | - Raffaella Ardau
- Section of Neurosciences and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Sardinia, Italy
| | - Edgar Arrua Vares
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Memduha Aydin
- Department of Psychiatry, Selcuk University Faculty of Medicine, Konya, Turkey
| | - Yavuz Ayhan
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Christopher Baethge
- Department of Psychiatry and Psychotherapy, University of Cologne Medical School, Cologne, Germany
| | - Rita Bauer
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Munster, Germany.,Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia.,The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Ceylan Balaban
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Johann Wolfgang Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | | | - Aniruddh P Behere
- Child and Adolescent Psychiatry, Helen DeVos Children's Hospital, Michigan State University-CHM, Grand Rapids, MI, USA
| | - Prakash B Behere
- Department of Psychiatry, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences (Deemed University), Wardha, India
| | - Habte Belete
- Department of Psychiatry, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Tilahun Belete
- Department of Psychiatry, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Gabriel Okawa Belizario
- Bipolar Disorder Research Program, Department of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil
| | - Frank Bellivier
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique-Hôpitaux de Paris, INSERM UMR-S1144, Université de Paris, FondaMental Foundation, Paris, France
| | - Robert H Belmaker
- Professor Emeritus of Psychiatry, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Francesco Benedetti
- University Vita-Salute San Raffaele, Milan, Italy.,Psychiatry and Clinical Psychobiology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Michael Berk
- Deakin University, IMPACT-The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia.,Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health, Department of Psychiatry, The University of Melbourne, Melbourne, Australia
| | - Yuly Bersudsky
- Department of Psychiatry, Faculty of Health Sciences, Beer Sheva Mental Health Center, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Şule Bicakci
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey.,Department of Psychiatry, Baskent University Faculty of Medicine, Ankara, Turkey
| | | | - Thomas D Bjella
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Conan Brady
- Department of Psychiatry, Trinity College Dublin, St Patrick's University Hospital, Dublin, Ireland
| | - Jorge Cabrera
- Mood Disorders Clinic, Dr. Jose Horwitz Psychiatric Institute, Santiago de Chile, Chile
| | | | - Angela Marianne Paredes Castro
- Deakin University, IMPACT-The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
| | - Wei-Ling Chen
- Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, Chiayi, Taiwan
| | | | - Silvia Chiesa
- Department of Mental Health and Substance Abuse, Piacenza, Italy
| | - Marie Crowe
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Alessandro Cuomo
- Department of Molecular Medicine, University of Siena School of Medicine, Siena, Italy
| | - Sara Dallaspezia
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Maria Del Zompo
- Section of Neurosciences and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Sardinia, Italy
| | | | - Seetal Dodd
- Deakin University, IMPACT-The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia.,Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Markus Donix
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Bruno Etain
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique-Hôpitaux de Paris, INSERM UMR-S1144, Université de Paris, FondaMental Foundation, Paris, France
| | - Andrea Fagiolini
- Department of Molecular Medicine, University of Siena School of Medicine, Siena, Italy
| | - Frederike T Fellendorf
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | | | - Jess G Fiedorowicz
- Department of Psychiatry, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Kostas N Fountoulakis
- 3rd Department of Psychiatry, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Mark A Frye
- Department of Psychiatry and Psychology, Mayo Clinic Depression Center, Mayo Clinic, Rochester, MN, USA
| | - Pierre A Geoffroy
- Département de Psychiatrie et d'addictologie, AP-HP, Hopital Bichat-Claude Bernard, Paris, France.,GHU Paris-Psychiatry and Neurosciences, 75014, Paris, France.,Université de Paris, NeuroDiderot, Inserm, Paris, France
| | - Ana Gonzalez-Pinto
- BIOARABA, Department of Psychiatry, University Hospital of Alava, University of the Basque Country, CIBERSAM, Vitoria, Spain
| | - John F Gottlieb
- Department of Psychiatry, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Paul Grof
- Mood Disorders Center of Ottawa and the Department of Psychiatry, University of Toronto, Ottawa, Canada
| | - Bartholomeus C M Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Hirohiko Harima
- Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital, Setagaya, Tokyo, Japan
| | - Mathias Hasse-Sousa
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Chantal Henry
- Department of Psychiatry, GHU Paris Psychiatrie & Neurosciences, 75014, Paris, France.,Université de Paris, 75006, Paris, France
| | - Lone Høffding
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Josselin Houenou
- Université Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, 94010, Créteil, France.,Université Paris Saclay, CEA, Neurospin, 91191, Gif-sur-Yvette, France
| | | | - Erkki T Isometsä
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,National Institute for Health and Welfare, Helsinki, Finland
| | - Maja Ivkovic
- University Clinical Center of Serbia, Clinic for Psychiatry, Belgrade, Serbia
| | - Sven Janno
- Department of Psychiatry, University of Tartu, Tartu, Estonia
| | - Simon Johnsen
- Unit for Psychiatric Research, Aalborg University Hospital, Aalborg, Denmark
| | - Flávio Kapczinski
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Gregory N Karakatsoulis
- 3rd Department of Psychiatry, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Mathias Kardell
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Seong Jae Kim
- Department of Psychiatry, Cheongju Hospital, Cheongju, South Korea
| | - Barbara König
- BIPOLAR Zentrum Wiener Neustadt, Wiener Neustadt, Austria
| | - Timur L Kot
- Khanty-Mansiysk Clinical Psychoneurological Hospital, Khanty-Mansiysk, Russia
| | - Michael Koval
- Department of Neuroscience, Michigan State University, East Lansing, MI, USA
| | - Mauricio Kunz
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Beny Lafer
- Bipolar Disorder Research Program, Department of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil
| | - Mikael Landén
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik R Larsen
- Mental Health Department Odense, University Clinic and Department of Regional Health Research, University of Southern Denmark, Esbjerg, Denmark
| | - Melanie Lenger
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | - Ute Lewitzka
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Rasmus W Licht
- Psychiatry, Aalborg University Hospital, Aalborg, Denmark.,Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Carlos Lopez-Jaramillo
- Mood Disorders Program, Hospital Universitario San Vicente Fundación, Research Group in Psychiatry, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia
| | - Alan MacKenzie
- Forensic Psychiatry, University of Glasgow, NHS Greater Glasgow and Clyde, Glasgow, UK
| | | | | | - Jayant Mahadevan
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Agustine Mahardika
- Department of Psychiatry, Faculty of Medicine, Mataram University, Mataram, Indonesia
| | - Mirko Manchia
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada.,Section of Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy.,Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Wendy Marsh
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Klaus Martiny
- Copenhagen University Hospitals, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Yuki Mashima
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Declan M McLoughlin
- Department of Psychiatry, Trinity College Institute of Neuroscience, Trinity College Dublin, St Patrick's University Hospital, Dublin, Ireland
| | - Ybe Meesters
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ingrid Melle
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Fátima Meza-Urzúa
- National Institute of Psychiatry "Ramón de la Fuente Muñiz", Mexico City, Mexico
| | - Mok Yee Ming
- Department of General Psychiatry, Mood Disorders Unit, Institute of Mental Health, Singapore City, Singapore
| | - Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA
| | - Muthukumaran Moorthy
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Gunnar Morken
- Department of Mental Health, Norwegian University of Science and Technology-NTNU, Trondheim, Norway.,Department of Psychiatry, St Olavs' University Hospital, Trondheim, Norway
| | - Enrica Mosca
- Section of Neurosciences and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Sardinia, Italy
| | | | - Rodrigo Munoz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | | | - Fethi Nacef
- Razi Hospital, Faculty of Medicine, University of Tunis-El Manar, Tunis, Tunisia
| | - Ravi K Nadella
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Takako Nakanotani
- Affective Disorders Research Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo, Japan
| | - René Ernst Nielsen
- Psychiatry, Aalborg University Hospital, Aalborg, Denmark.,Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Claire O'Donovan
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Adel Omrani
- Tunisian Bipolar Forum, Érable Médical Cabinet 324, Lac 2, Tunis, Tunisia
| | - Yamima Osher
- Department of Psychiatry, Faculty of Health Sciences, Beer Sheva Mental Health Center, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Uta Ouali
- Razi Hospital, Faculty of Medicine, University of Tunis-El Manar, Tunis, Tunisia
| | | | - Pornjira Pariwatcharakul
- Department of Psychiatry, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Joanne Petite
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | - Maximilian Pilhatsch
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Department of Psychiatry and Psychotherapy, Elblandklinikum Radebeul, Radebeul, Germany
| | - Marco Pinna
- Section of Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy.,Lucio Bini Mood Disorder Center, Cagliari, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health and Sensory Organs, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Richard Porter
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Danilo Quiroz
- Deparment of Psychiatry, Diego Portales University, Santiago de Chile, Chile
| | | | - Raj Ramesar
- SA MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Natalie Rasgon
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Palo Alto, CA, USA
| | - Woraphat Ratta-Apha
- Department of Psychiatry, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Michaela Ratzenhofer
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | - Maria Redahan
- Department of Psychiatry, Trinity College Dublin, St Patrick's University Hospital, Dublin, Ireland
| | - M S Reddy
- Asha Bipolar Clinic, Asha Hospital, Hyderabad, Telangana, India
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Johann Wolfgang Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University Graz, Graz, Austria
| | - Jenny Gringer Richards
- Departments of Psychiatry, Epidemiology, and Internal Medicine, Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Janusz K Rybakowski
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Leela Sathyaputri
- Departments of Psychiatry, Epidemiology, and Internal Medicine, Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA
| | - Ângela M Scippa
- Department of Neuroscience and Mental Health, Federal University of Bahia, Salvador, Brazil
| | - Christian Simhandl
- Bipolar Zentrum Wiener Neustadt, Sigmund Freud Privat Universität, Vienna, Austria
| | - Emanuel Severus
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Daniel Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, UK
| | - José Smith
- Bipolar Disorder Program, Neuroscience Institute, Favaloro University, Buenos Aires, Argentina
| | - Paul W Stackhouse
- Science Directorate/Climate Science Branch, NASA Langley Research Center, Hampton, VA, USA
| | - Dan J Stein
- Department of Psychiatry, MRC Unit On Risk and Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa
| | - Kellen Stilwell
- Pine Rest Christian Mental Health Services, Grand Rapids, MI, USA
| | - Sergio Strejilevich
- Bipolar Disorder Program, Neuroscience Institute, Favaloro University, Buenos Aires, Argentina
| | - Kuan-Pin Su
- College of Medicine, China Medical University (CMU), Taichung, Taiwan.,An-Nan Hospital, China Medical University, Tainan, Taiwan
| | | | - Ahmad Hatim Sulaiman
- Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kirsi Suominen
- Department of Social Services and Health Care, Psychiatry, City of Helsinki, Helsinki, Finland
| | - Andi J Tanra
- Department of Psychiatry, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
| | - Yoshitaka Tatebayashi
- Affective Disorders Research Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo, Japan
| | - Wen Lin Teh
- Research Division, Institute of Mental Health, Singapore, Singapore
| | - Leonardo Tondo
- McLean Hospital-Harvard Medical School, Boston, MA, USA.,Mood Disorder Lucio Bini Centers, Cagliari e Roma, Italy
| | - Carla Torrent
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Daniel Tuinstra
- Pine Rest Christian Mental Health Services, Grand Rapids, MI, USA
| | - Takahito Uchida
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Arne E Vaaler
- Department of Mental Health, Norwegian University of Science and Technology-NTNU, Trondheim, Norway.,Department of Psychiatry, St Olavs' University Hospital, Trondheim, Norway
| | - Julia Veeh
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Johann Wolfgang Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Eduard Vieta
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Biju Viswanath
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India
| | - Maria Yoldi-Negrete
- Subdirección de Investigaciones Clínicas, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñíz, Mexico City, Mexico
| | - Oguz Kaan Yalcinkaya
- Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Allan H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yosra Zgueb
- Razi Hospital, Faculty of Medicine, University of Tunis-El Manar, Tunis, Tunisia
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
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24
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Xu X, Zhu H, Hoang AQ, Ahn C. Sample size considerations for matched-pair cluster randomization design with incomplete observations of binary outcomes. Stat Med 2021; 40:5397-5416. [PMID: 34245031 DOI: 10.1002/sim.9131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/24/2021] [Accepted: 06/22/2021] [Indexed: 11/05/2022]
Abstract
Multiple public health and medical research studies have applied matched-pair cluster randomization design to the evaluation of the intervention and/or prevention effects. One of the most common and severe problems faced by researchers when conducting cluster randomized trials (CRTs) is incomplete observations, which are associated with various reasons causing the individuals to discontinue participating in the trials. Although statistical methods to remedy the problems of missing data have already been proposed, there are still methodological gaps in research concerning the determination of sample size in matched-pair CRTs with incomplete binary outcomes. One conventional method for adjusting for missing data in the sample size determination is to divide the sample size under complete data by the expected follow-up rate. However, such crude adjustment ignores the impact of the structure and strength of correlations regarding both outcome data and missing data mechanism. This article provides a closed-form sample size formula for matched-pair CRTs with incomplete binary outcomes, which appropriately accounts for different missing patterns and magnitudes as well as the effects of matching and clustering on the outcome and missing data. The generalized estimating equation (GEE) approach treats incomplete observations as missing data in a marginal logistic regression model, which flexibly accommodates various types of intraclass correlation, missing patterns, and missing proportions. In the presence of missing data, the proposed GEE sample size method provides higher accuracy as compared with the conventional method. The performance of the proposed method is assessed by simulation studies. This article also illustrates how the proposed method can be used to design a real-world matched-pair CRT to examine the effect of a team-based approach on controlling blood pressure (BP).
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Affiliation(s)
- Xiaohan Xu
- Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA
| | - Hong Zhu
- Division of Biostatistics, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Anh Q Hoang
- Department of Mathematical Sciences, University of Texas at Dallas, Dallas, Texas, USA
| | - Chul Ahn
- Division of Biostatistics, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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25
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Korevaar E, Kasza J, Taljaard M, Hemming K, Haines T, Turner EL, Thompson JA, Hughes JP, Forbes AB. Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials. Clin Trials 2021; 18:529-540. [PMID: 34088230 DOI: 10.1177/17407745211020852] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. METHODS Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. RESULTS The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02-0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19-0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. DISCUSSION This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.
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Affiliation(s)
- Elizabeth Korevaar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Terry Haines
- School of Primary and Allied Health Care, Monash University, Melbourne, VIC, Australia
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.,Duke Global Health Institute, Durham, NC, USA
| | - Jennifer A Thompson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - James P Hughes
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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26
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Li F, Tong G. Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome. Stat Methods Med Res 2021; 30:1288-1305. [PMID: 33826454 PMCID: PMC9132618 DOI: 10.1177/0962280221990415] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The modified Poisson regression coupled with a robust sandwich variance has become a viable alternative to log-binomial regression for estimating the marginal relative risk in cluster randomized trials. However, a corresponding sample size formula for relative risk regression via the modified Poisson model is currently not available for cluster randomized trials. Through analytical derivations, we show that there is no loss of asymptotic efficiency for estimating the marginal relative risk via the modified Poisson regression relative to the log-binomial regression. This finding holds both under the independence working correlation and under the exchangeable working correlation provided a simple modification is used to obtain the consistent intraclass correlation coefficient estimate. Therefore, the sample size formulas developed for log-binomial regression naturally apply to the modified Poisson regression in cluster randomized trials. We further extend the sample size formulas to accommodate variable cluster sizes. An extensive Monte Carlo simulation study is carried out to validate the proposed formulas. We find that the proposed formulas have satisfactory performance across a range of cluster size variability, as long as suitable finite-sample corrections are applied to the sandwich variance estimator and the number of clusters is at least 10. Our findings also suggest that the sample size estimate under the exchangeable working correlation is more robust to cluster size variability, and recommend the use of an exchangeable working correlation over an independence working correlation for both design and analysis. The proposed sample size formulas are illustrated using the Stop Colorectal Cancer (STOP CRC) trial.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Preventive Science, Yale University, New Haven, CT, USA
- Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA
| | - Guangyu Tong
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA
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27
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Li F, Tong G. Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation. Biom J 2021; 63:1052-1071. [PMID: 33751620 DOI: 10.1002/bimj.202000230] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/01/2021] [Accepted: 01/09/2021] [Indexed: 01/03/2023]
Abstract
Cluster randomized trials (CRTs) are widely used in epidemiological and public health studies assessing population-level effect of group-based interventions. One important application of CRTs is the control of vector-borne disease, such as malaria. However, a particular challenge for designing these trials is that the primary outcome involves counts of episodes that are subject to right truncation. While sample size formulas have been developed for CRTs with clustered counts, they are not directly applicable when the counts are right truncated. To address this limitation, we discuss two marginal modeling approaches for the analysis of CRTs with truncated counts and develop two corresponding closed-form sample size formulas to facilitate the design of such trials. The proposed sample size formulas allow investigators to explore the power under a large number of scenarios without computationally intensive simulations. The proposed formulas are validated in extensive simulations. We further explore the implication of right truncation on power and apply the proposed formulas to illustrate the power calculation for a malaria control CRT where the primary outcome is subject to right truncation.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.,Center for Methods in Implementation and Prevention Science, Yale University, New Haven, CT, USA.,Yale Center for Analytical Sciences, New Haven, CT, USA
| | - Guangyu Tong
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.,Yale Center for Analytical Sciences, New Haven, CT, USA
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28
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Tang Y. Power and sample size for GEE analysis of incomplete paired outcomes in 2 × 2 crossover trials. Pharm Stat 2021; 20:820-839. [PMID: 33738918 DOI: 10.1002/pst.2112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 01/26/2021] [Accepted: 02/26/2021] [Indexed: 11/11/2022]
Abstract
The 2 × 2 crossover trial uses subjects as their own control to reduce the intersubject variability in the treatment comparison, and typically requires fewer subjects than a parallel design. The generalized estimating equations (GEE) methodology has been commonly used to analyze incomplete discrete outcomes from crossover trials. We propose a unified approach to the power and sample size determination for the Wald Z-test and t-test from GEE analysis of paired binary, ordinal and count outcomes in crossover trials. The proposed method allows misspecification of the variance and correlation of the outcomes, missing outcomes, and adjustment for the period effect. We demonstrate that misspecification of the working variance and correlation functions leads to no or minimal efficiency loss in GEE analysis of paired outcomes. In general, GEE requires the assumption of missing completely at random. For bivariate binary outcomes, we show by simulation that the GEE estimate is asymptotically unbiased or only minimally biased, and the proposed sample size method is suitable under missing at random (MAR) if the working correlation is correctly specified. The performance of the proposed method is illustrated with several numerical examples. Adaption of the method to other paired outcomes is discussed.
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Affiliation(s)
- Yongqiang Tang
- Department of Biostatistics, Tesaro, Waltham, Massachusetts, USA
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29
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Li F, Hughes JP, Hemming K, Taljaard M, Melnick ER, Heagerty PJ. Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview. Stat Methods Med Res 2021; 30:612-639. [PMID: 32631142 PMCID: PMC7785651 DOI: 10.1177/0962280220932962] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The stepped wedge cluster randomized design has received increasing attention in pragmatic clinical trials and implementation science research. The key feature of the design is the unidirectional crossover of clusters from the control to intervention conditions on a staggered schedule, which induces confounding of the intervention effect by time. The stepped wedge design first appeared in the Gambia hepatitis study in the 1980s. However, the statistical model used for the design and analysis was not formally introduced until 2007 in an article by Hussey and Hughes. Since then, a variety of mixed-effects model extensions have been proposed for the design and analysis of these trials. In this article, we explore these extensions under a unified perspective. We provide a general model representation and regard various model extensions as alternative ways to characterize the secular trend, intervention effect, as well as sources of heterogeneity. We review the key model ingredients and clarify their implications for the design and analysis. The article serves as an entry point to the evolving statistical literatures on stepped wedge designs.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Preventive Science, Yale University, New Haven, CT, USA
| | - James P Hughes
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Edward R. Melnick
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Patrick J Heagerty
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
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30
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Yang S, Li F, Starks MA, Hernandez AF, Mentz RJ, Choudhury KR. Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials. Stat Med 2020; 39:4218-4237. [PMID: 32823372 PMCID: PMC7948251 DOI: 10.1002/sim.8721] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 12/14/2022]
Abstract
Cluster randomized trials (CRTs) refer to experiments with randomization carried out at the cluster or the group level. While numerous statistical methods have been developed for the design and analysis of CRTs, most of the existing methods focused on testing the overall treatment effect across the population characteristics, with few discussions on the differential treatment effect among subpopulations. In addition, the sample size and power requirements for detecting differential treatment effect in CRTs remain unclear, but are helpful for studies planned with such an objective. In this article, we develop a new sample size formula for detecting treatment effect heterogeneity in two-level CRTs for continuous outcomes, continuous or binary covariates measured at cluster or individual level. We also investigate the roles of two intraclass correlation coefficients (ICCs): the adjusted ICC for the outcome of interest and the marginal ICC for the covariate of interest. We further derive a closed-form design effect formula to facilitate the application of the proposed method, and provide extensions to accommodate multiple covariates. Extensive simulations are carried out to validate the proposed formula in finite samples. We find that the empirical power agrees well with the prediction across a range of parameter constellations, when data are analyzed by a linear mixed effects model with a treatment-by-covariate interaction. Finally, we use data from the HF-ACTION study to illustrate the proposed sample size procedure for detecting heterogeneous treatment effects.
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Affiliation(s)
- Siyun Yang
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut
| | - Monique A. Starks
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Adrian F. Hernandez
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Robert J. Mentz
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Kingshuk R. Choudhury
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
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31
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Yu H, Li F, Turner EL. An evaluation of quadratic inference functions for estimating intervention effects in cluster randomized trials. Contemp Clin Trials Commun 2020; 19:100605. [PMID: 32728648 PMCID: PMC7381491 DOI: 10.1016/j.conctc.2020.100605] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 06/15/2020] [Accepted: 06/28/2020] [Indexed: 01/02/2023] Open
Abstract
Cluster randomized trials (CRTs) usually randomize groups of individuals to interventions, and outcomes are typically measured at the individual level. Marginal intervention effects are frequently of interest in CRTs due to their population-averaged interpretations. Such effects are estimated using generalized estimating equations (GEE), or a recent alternative called the quadratic inference function (QIF). However, the performance of QIF relative to GEE have not been extensively evaluated in the CRT context, especially when the marginal mean model includes additional covariates. Motivated by the HALI trial, we conduct simulation studies to compare the finite-sample operating characteristics of QIF and GEE. We demonstrate that QIF and GEE are equivalent under some conditions. When the marginal mean model includes individual-level covariates, QIF shows an efficiency improvement over GEE with overall larger power, but its test size may be more liberal than GEE and GEE achieves better coverage than QIF. The test size inflation may not by fully addressed from using finite-sample bias corrections. The estimates of QIF tend to be closer to GEE in the HALI data, although the former presents a small standard error. Overall, we confirm that the QIF approach generally has potentially better efficiency than GEE in our simulation studies but might be more cautiously used as a viable approach for the analysis of CRTs. More research is needed, however, to address the finite-sample bias in the variance estimation of the QIF to better control its test size.
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Affiliation(s)
- Hengshi Yu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Corresponding author.
| | - Fan Li
- Department of Biostatistics, Yale University, New Haven, CT, 06510, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, CT, 06510, USA
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, 27710, USA
- Duke Global Health Institute, Durham, NC, 27710, USA
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32
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Allore HG, Goldfeld KS, Gutman R, Li F, Monin JK, Taljaard M, Travison TG. Statistical Considerations for Embedded Pragmatic Clinical Trials in People Living with Dementia. J Am Geriatr Soc 2020; 68 Suppl 2:S68-S73. [PMID: 32589276 DOI: 10.1111/jgs.16616] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 04/09/2020] [Accepted: 04/10/2020] [Indexed: 12/17/2022]
Abstract
There is overwhelming need for nonpharmacological interventions to improve the health and well-being of people living with dementia (PLWD). The National Institute on Aging Imbedded Pragmatic Alzheimer's Disease (AD) and AD-Related Dementias Clinical Trials (IMPACT) Collaboratory supports clinical trials of such interventions embedded in healthcare systems. The embedded pragmatic clinical trial (ePCT) is ideally suited to testing the effectiveness of complex interventions in vulnerable populations at the point of care. These trials, however, are complex to conduct and interpret, and face challenges in efficiency (i.e., statistical power) and reproducibility. In addition, trials conducted among PLWD present specific statistical challenges, including difficulty in outcomes ascertainment from PLWD, necessitating reliance on reports by caregivers, and heterogeneity in measurements across different settings or populations. These and other challenges undercut the reliability of measurement, the feasibility of capturing outcomes using pragmatic designs, and the ability to validly estimate interventions' effectiveness in real-world settings. To address these challenges, the IMPACT Collaboratory has convened a Design and Statistics Core, the goals of which are: to support the design and conduct of ePCTs directed toward PLWD and their caregivers; to develop guidance for conducting embedded trials in this population; and to educate quantitative and clinical scientists in the design, conduct, and analysis of these trials. In this article, we discuss some of the contemporary methodological challenges in this area and develop a set of research priorities the Design and Statistics Core will undertake to meet these goals. J Am Geriatr Soc 68:S68-S73, 2020.
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Affiliation(s)
- Heather G Allore
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, USA.,Section of Geriatrics, Department of Internal Medicine, School of Medicine, Yale University, New Haven, Connecticut, USA
| | - Keith S Goldfeld
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
| | - Roee Gutman
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Fan Li
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, USA
| | - Joan K Monin
- Department of Social and Behavioral Sciences, School of Public Health, Yale University, New Haven, Connecticut, USA
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Thomas G Travison
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.,Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA
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33
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Li F, Harhay MO. Commentary: Right truncation in cluster randomized trials can attenuate the power of a marginal analysis. Int J Epidemiol 2020; 49:964-967. [PMID: 32211886 PMCID: PMC7394942 DOI: 10.1093/ije/dyaa037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2020] [Indexed: 01/12/2023] Open
Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research (PAIR) Center and Pulmonary and Critical Care Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA
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34
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Gallis JA, Li F, Turner EL. xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials. THE STATA JOURNAL 2020; 20:363-381. [PMID: 35330784 PMCID: PMC8942127 DOI: 10.1177/1536867x20931001] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.
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Affiliation(s)
- John A Gallis
- Department of Biostatistics and Bioinformatics, Duke University, Duke Global Health Institute, Durham, NC
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Duke Global Health Institute, Durham, NC
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35
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Li F. Design and analysis considerations for cohort stepped wedge cluster randomized trials with a decay correlation structure. Stat Med 2019; 39:438-455. [PMID: 31797438 DOI: 10.1002/sim.8415] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 08/21/2019] [Accepted: 10/07/2019] [Indexed: 01/08/2023]
Abstract
A stepped wedge cluster randomized trial is a type of longitudinal cluster design that sequentially switches clusters to intervention over time until all clusters are treated. While the traditional posttest-only parallel design requires adjustment for a single intraclass correlation coefficient, the stepped wedge design allows multiple outcome measurements from the same cluster and so additional correlation parameters are necessary to characterize the within-cluster correlation structure. Although a number of studies have differentiated between the concepts of within-period and between-period correlations, only a few studies have allowed the between-period correlation to decay over time. In this article, we consider the proportional decay correlation structure for a cohort stepped wedge design, and provide a matrix-adjusted quasi-least squares approach to accurately estimate the correlation parameters along with the marginal intervention effect. We further develop the sample size and power procedures accounting for the correlation decay, and investigate the accuracy of the power procedure with continuous outcomes in a simulation study. We show that the empirical power agrees well with the prediction even with as few as nine clusters, when data are analyzed with matrix-adjusted quasi-least squares concurrently with a suitable bias-corrected sandwich variance. Two trial examples are provided to illustrate the new sample size procedure.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University, New Haven, Connecticut.,Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut
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36
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Grantham KL, Kasza J, Heritier S, Hemming K, Litton E, Forbes AB. How many times should a cluster randomized crossover trial cross over? Stat Med 2019; 38:5021-5033. [PMID: 31475383 DOI: 10.1002/sim.8349] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 07/17/2019] [Accepted: 07/26/2019] [Indexed: 01/18/2023]
Abstract
Trial planning requires making efficient yet practical design choices. In a cluster randomized crossover trial, clusters of subjects cross back and forth between implementing the control and intervention conditions over the course of the trial, with each crossover marking the start of a new period. If it is possible to set up such a trial with more crossovers, a pertinent question is whether there are efficiency gains from clusters crossing over more frequently, and if these gains are substantial enough to justify the added complexity and cost of implementing more crossovers. We seek to determine the optimal number of crossovers for a fixed trial duration, and then identify other highly efficient designs by allowing the total number of clusters to vary and imposing thresholds on maximum cost and minimum statistical power. Our results pertain to trials with continuous recruitment and a continuous primary outcome, with the treatment effect estimated using a linear mixed model. To account for the similarity between subjects' outcomes within a cluster, we assume a correlation structure in which the correlation decays gradually in a continuous manner as the time between subjects' measurements increases. The optimal design is characterized by crossovers between the control and intervention conditions with each successive subject. However, this design is neither practical nor cost-efficient to implement, nor is it necessary: the gains in efficiency increase sharply in moving from a two-period to a four-period trial design, but approach an asymptote for the scenarios considered as the number of crossovers continues to increase.
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Affiliation(s)
- Kelsey L Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Edward Litton
- Intensive Care Unit, Fiona Stanley Hospital, Murdoch, Australia
- School of Medicine, University of Western Australia, Perth, Australia
- Centre for Outcome and Resource Evaluation, Australian and New Zealand Intensive Care Society, Camberwell, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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37
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Turner EL, Yao L, Li F, Prague M. Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness. Stat Methods Med Res 2019; 29:1338-1353. [DOI: 10.1177/0962280219859915] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE vs. MMI-GEE for binary outcomes. Simulated data are generated assuming a covariate-dependent missing data pattern across a range of missingness clustering (from none to high), where all covariates are measured at baseline and are fully observed (i.e. a type of missing-at-random mechanism). Two types of weights are estimated and used in the weighted GEE: (1) assuming no clustering of missingness (W-GEE) and (2) accounting for such clustering (CW-GEE). Results show that, even in settings with high missingness clustering, CW-GEE can lead to more bias and lower coverage than W-GEE, whereas W-GEE and MMI-GEE provide comparable results. W-GEE should be considered a viable strategy to account for missing outcomes in cluster randomized trials.
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Affiliation(s)
- Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Lanqiu Yao
- Department of Population Health, New York University, New York, NY, USA
| | - Fan Li
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Melanie Prague
- INRIA SISTM, Inserm U1219 Bordeaux Population Health, Université Bordeaux, ISPED, Bordeaux, France
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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