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Mulawa MI, Docherty SL, Bailey DE, Gonzalez-Guarda RM, Lipkus IM, Randolph SD, Yang Q, Pan W. A Hybrid Pragmatic and Factorial Cluster Randomized Controlled Trial for an Anti-racist, Multilevel Intervention to Improve Mental Health Equity in High Schools. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024; 25:486-496. [PMID: 38175459 PMCID: PMC11239747 DOI: 10.1007/s11121-023-01626-x] [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: 12/10/2023] [Indexed: 01/05/2024]
Abstract
Systemic racism is pervasive in US society and disproportionately limits opportunities for education, work, and health for historically marginalized and minoritized racial and ethnic groups, making it an urgent issue of social justice. Because systemic racism is a social determinant of health prevalent across multiple social and institutional structures, it requires multilevel intervention approaches using effective designs and analytic methods to measure and evaluate outcomes. Racism is a fundamental cause of poor health outcomes, including mental health outcomes; thus, mental health services and programs that address racism and discrimination are key to promoting positive mental health of racial and ethnic minority youth. While multilevel interventions are well-suited for improving outcomes like youth mental health disparities, their evaluation poses unique methodological challenges, requiring specialized design and analytic approaches. There has been limited methodological guidance provided to researchers on how to test multilevel interventions using approaches that balance methodological rigor, practicality, and acceptability across stakeholder groups, especially within communities most affected by systemic racism. This paper addresses this gap by providing an example of how to rigorously evaluate a hypothetical, theoretically based, multilevel intervention promoting mental health equity in three US school systems using an anti-racist approach intervening at the macro- (i.e., school system), meso- (i.e., school), and micro- (i.e., family and student) levels to improve mental health in adolescents. We describe the design, sample size considerations, and analytic methods to comprehensively evaluate its effectiveness while exploring the extent to which the components interact synergistically to improve outcomes. The methodological approach proposed can be adapted to other multilevel interventions that include strategies addressing macro-, meso-, and micro-levels of influence.
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Affiliation(s)
- Marta I Mulawa
- Duke University School of Nursing, Duke University, Durham, NC, USA.
- Duke Global Health Institute, Duke University, Durham, NC, USA.
| | - Sharron L Docherty
- Duke University School of Nursing, Duke University, Durham, NC, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA
| | - Donald E Bailey
- Duke University School of Nursing, Duke University, Durham, NC, USA
| | | | - Isaac M Lipkus
- Duke University School of Nursing, Duke University, Durham, NC, USA
| | | | - Qing Yang
- Duke University School of Nursing, Duke University, Durham, NC, USA
| | - Wei Pan
- Duke University School of Nursing, Duke University, Durham, NC, USA
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
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Wang X, Goldfeld KS, Taljaard M, Li F. Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024; 25:356-370. [PMID: 37816835 PMCID: PMC11004667 DOI: 10.1007/s11121-023-01590-6] [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: 10/02/2023] [Indexed: 10/12/2023]
Abstract
Cluster-randomized trials (CRTs) often allocate intact clusters of participants to treatment or control conditions and are increasingly used to evaluate healthcare delivery interventions. While previous studies have developed sample size methods for testing confirmatory hypotheses of treatment effect heterogeneity in CRTs (i.e., targeting the difference between subgroup-specific treatment effects), sample size methods for testing the subgroup-specific treatment effects themselves have not received adequate attention-despite a rising interest in health equity considerations in CRTs. In this article, we develop formal methods for sample size and power analyses for testing subgroup-specific treatment effects in parallel-arm CRTs with a continuous outcome and a binary subgroup variable. We point out that the variances of the subgroup-specific treatment effect estimators and their covariance are given by weighted averages of the variance of the overall average treatment effect estimator and the variance of the heterogeneous treatment effect estimator. This analytical insight facilitates an explicit characterization of the requirements for both the omnibus test and the intersection-union test to achieve the desired level of power. Generalizations to allow for subgroup-specific variance structures are also discussed. We report on a simulation study to validate the proposed sample size methods and demonstrate that the empirical power corresponds well with the predicted power for both tests. The design and setting of the Umea Dementia and Exercise (UMDEX) CRT in older adults are used to illustrate our sample size methods.
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Affiliation(s)
- Xueqi Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 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, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, 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, Suite 200, Room 229, 135 College Street, New Haven, CT, 06510, USA.
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Guilamo-Ramos V, Thimm-Kaiser M, Benzekri A, Johnson C, Williams D, Wilhelm-Hilkey N, Goodman M, Hagan H. Application of a Heuristic Framework for Multilevel Interventions to Eliminate the Impact of Unjust Social Processes and Other Harmful Social Determinants of Health. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024; 25:446-458. [PMID: 38607535 PMCID: PMC11239765 DOI: 10.1007/s11121-024-01658-x] [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: 02/20/2024] [Indexed: 04/13/2024]
Abstract
There is consensus about the importance of developing a strong cadre of effective multilevel interventions to eliminate the impacts of unjust social processes, such as structural racism and other harmful social determinants of health (SDOH), on health inequities in the USA. However, the available cadre of rigorously evaluated evidence-based interventions for SDOH mitigation remains underdeveloped relative to the magnitude of historic and current health inequities. The proposed manuscript addresses this gap in two ways: first, by introducing a heuristic framework to inform decisions in multilevel intervention development, study design, and selection of analytic methods and, second, by providing a roadmap for future applications of the framework in multilevel intervention research through an exemplar application using the ongoing NIH-funded evaluation study of the Nurse-Community-Family Partnership (NCFP) intervention. NCFP leverages individual, family, institutional, and system factors to shape COVID-19 mitigation outcomes at the individual and household levels. NCFP takes an approach informed by the heuristic framework to addressing and mitigating unjust social processes and other harmful SDOH. We discuss the application of a two-arm parallel explanatory group randomized trial to evaluate the efficacy of NCFP in improving the primary (COVID-19 testing uptake) and secondary (adoption of COVID-19 control measures, COVID-19 vaccine uptake, mutual aid capacity, etc.) outcomes at the individual and household levels. The analysis approach relies on random-intercept models, and we calculate the variance partitioning coefficient to estimate the extent to which household- and individual-level variables contribute to the outcome, allowing examination of NCFP effects at multiple levels.
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Affiliation(s)
- Vincent Guilamo-Ramos
- Center for Latino Adolescent and Family Health, Johns Hopkins University, Washington, DC, 20001, USA.
- Institute for Policy Solutions, Johns Hopkins University School of Nursing, Johns Hopkins University, Washington, DC, 20001, USA.
- School of Nursing, Johns Hopkins University, Baltimore, MD, 21205, USA.
- Presidential Advisory Council On HIV/AIDS, US Department of Health and Human Services, Washington, DC, USA.
| | - Marco Thimm-Kaiser
- Center for Latino Adolescent and Family Health, Johns Hopkins University, Washington, DC, 20001, USA
- Institute for Policy Solutions, Johns Hopkins University School of Nursing, Johns Hopkins University, Washington, DC, 20001, USA
- School of Nursing, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Adam Benzekri
- Center for Latino Adolescent and Family Health, Johns Hopkins University, Washington, DC, 20001, USA
- Institute for Policy Solutions, Johns Hopkins University School of Nursing, Johns Hopkins University, Washington, DC, 20001, USA
- School of Nursing, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Celia Johnson
- Center for Latino Adolescent and Family Health, Johns Hopkins University, Washington, DC, 20001, USA
- Institute for Policy Solutions, Johns Hopkins University School of Nursing, Johns Hopkins University, Washington, DC, 20001, USA
- School of Nursing, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Desiree Williams
- Department of Epidemiology, School of Global Public Health, New York University, New York, NY, 10003, USA
- Department of Social Behavioral Sciences, School of Global Public Health, New York University, New York, NY, 10003, USA
| | - Nash Wilhelm-Hilkey
- Center for Latino Adolescent and Family Health, Johns Hopkins University, Washington, DC, 20001, USA
| | - Melody Goodman
- Department of Biostatistics, School of Global Public Health, New York University, New York, NY, 10003, USA
| | - Holly Hagan
- Department of Epidemiology, School of Global Public Health, New York University, New York, NY, 10003, USA
- Department of Social Behavioral Sciences, School of Global Public Health, New York University, New York, NY, 10003, USA
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Kahan BC, Blette BS, Harhay MO, Halpern SD, Jairath V, Copas A, Li F. Demystifying estimands in cluster-randomised trials. Stat Methods Med Res 2024:9622802241254197. [PMID: 38780480 DOI: 10.1177/09622802241254197] [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: 05/25/2024]
Abstract
Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned with the study's objectives. Cluster-randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster-specific, and whether they are participant- or cluster-average. In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster-level summaries. Then, through a re-analysis of a published cluster-randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 (p = 0.17) to 1.83 (p = 0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, is essential to ensuring that cluster-randomised trials address the right question.
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Affiliation(s)
- Brennan C Kahan
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - Bryan S Blette
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA
| | - Michael O Harhay
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Scott D Halpern
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Vipul Jairath
- Department of Medicine, Division of Gastroenterology, Schulich School of Medicine, Western University, London, ON, Canada
- Department of Epidemiology and Biostatistics, Western University, London, ON, Canada
| | - Andrew Copas
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, CT, USA
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Blette BS, Halpern SD, Li F, Harhay MO. Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials. Stat Methods Med Res 2024; 33:909-927. [PMID: 38567439 PMCID: PMC11041086 DOI: 10.1177/09622802241242323] [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: 04/04/2024]
Abstract
Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues.
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Affiliation(s)
- Bryan S Blette
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott D Halpern
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Clinical Trials Methods and Outcomes Lab, PAIR (Palliative and Advanced Illness Research) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - 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
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Clinical Trials Methods and Outcomes Lab, PAIR (Palliative and Advanced Illness Research) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Westgate PM, Nigam SR, Shoben AB. Reconsidering stepped wedge cluster randomized trial designs with implementation periods: Fewer sequences or the parallel-group design with baseline and implementation periods are potentially more efficient. Clin Trials 2024:17407745241244790. [PMID: 38650332 DOI: 10.1177/17407745241244790] [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] [Indexed: 04/25/2024]
Abstract
BACKGROUND/AIMS When designing a cluster randomized trial, advantages and disadvantages of tentative designs must be weighed. The stepped wedge design is popular for multiple reasons, including its potential to increase power via improved efficiency relative to a parallel-group design. In many realistic settings, it will take time for clusters to fully implement the intervention. When designing the HEALing (Helping to End Addiction Long-termSM) Communities Study, implementation time was a major consideration, and we examined the efficiency and practicality of three designs. Specifically, a three-sequence stepped wedge design with implementation periods, a corresponding two-sequence modified design that is created by removing the middle sequence, and a parallel-group design with baseline and implementation periods. In this article, we study the relative efficiencies of these specific designs. More generally, we study the relative efficiencies of modified designs when the stepped wedge design with implementation periods has three or more sequences. We also consider different correlation structures. METHODS We compare efficiencies of stepped wedge designs with implementation periods consisting of three to nine sequences with a variety of corresponding designs. The three-sequence design is compared to the two-sequence modified design and to the parallel-group design with baseline and implementation periods analysed via analysis of covariance. Stepped wedge designs with implementation periods consisting of four or more sequences are compared to modified designs that remove all or a subset of 'middle' sequences. Efficiencies are based on the use of linear mixed effects models. RESULTS In the studied settings, the modified design is more efficient than the three-sequence stepped wedge design with implementation periods. The parallel-group design with baseline and implementation periods with analysis of covariance-based analysis is often more efficient than the three-sequence design. With respect to stepped wedge designs with implementation periods that are comprised of more sequences, there are often corresponding modified designs that improve efficiency. However, use of only the first and last sequences has the potential to be either relatively efficient or inefficient. Relative efficiency is impacted by the strength of the statistical correlation among outcomes from the same cluster; for example, the relative efficiencies of modified designs tend to be greater for smaller cluster auto-correlation values. CONCLUSION If a three-sequence stepped wedge design with implementation periods is being considered for a future cluster randomized trial, then a corresponding modified design using only the first and last sequences should be considered if sole focus is on efficiency. However, a parallel-group design with baseline and implementation periods and analysis of covariance-based analysis can be a practical, efficient alternative. For stepped wedge designs with implementation periods and a larger number of sequences, modified versions that remove 'middle' sequences should be considered. Due to the potential sensitivity of design efficiencies, statistical correlation should be carefully considered.
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Affiliation(s)
- Philip M Westgate
- Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Shawn R Nigam
- Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Abigail B Shoben
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
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Wang W, Tong G, Hirani SP, Newman SP, Halpern SD, Small DS, Li F, Harhay MO. A mixed model approach to estimate the survivor average causal effect in cluster-randomized trials. Stat Med 2024; 43:16-33. [PMID: 37985966 DOI: 10.1002/sim.9939] [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: 03/08/2022] [Revised: 09/05/2023] [Accepted: 10/12/2023] [Indexed: 11/22/2023]
Abstract
In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly.
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Affiliation(s)
- Wei Wang
- Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guangyu Tong
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- 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
| | | | - Stanton P Newman
- School of Health Sciences, City University London, London, UK
- Division of Medicine, University College London, London, UK
| | - Scott D Halpern
- Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dylan S Small
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, 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
| | - Michael O Harhay
- Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Zhu AY, Mitra N, Hemming K, Harhay MO, Li F. Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome. Biom J 2024; 66:e2200135. [PMID: 37035941 PMCID: PMC10562517 DOI: 10.1002/bimj.202200135] [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: 05/06/2022] [Revised: 11/20/2022] [Accepted: 02/08/2023] [Indexed: 04/11/2023]
Abstract
Cluster-randomized trials (CRTs) involve randomizing entire groups of participants-called clusters-to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually randomized trials, analytical methods for individual-level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual-level covariate adjustment strategies for estimating the participant-average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite-sample performance of the bias-corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant-average treatment effect. To illustrate the methods for individual-level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units.
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Affiliation(s)
- Angela Y. Zhu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Nandita Mitra
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Karla Hemming
- Department of Public Health, Epidemiology, and Biostatistics, University of Birmingham Institute of Applied Health Research, Birmingham B15 2TT, United Kingdom
| | - Michael O. Harhay
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, United States of America
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT 06510, United States of America
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Offorha BC, Walters SJ, Jacques RM. Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies. BMC Med Res Methodol 2023; 23:293. [PMID: 38093221 PMCID: PMC10717070 DOI: 10.1186/s12874-023-02107-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. METHODS Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. RESULTS The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. CONCLUSION This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches.
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Affiliation(s)
- Bright C Offorha
- Division of Population Health, School of Medicine & Population Health, University of Sheffield, Sheffield, UK.
| | - Stephen J Walters
- Division of Population Health, School of Medicine & Population Health, University of Sheffield, Sheffield, UK
| | - Richard M Jacques
- Division of Population Health, School of Medicine & Population Health, University of Sheffield, Sheffield, UK
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Kahan BC, Li F, Blette B, Jairath V, Copas A, Harhay M. Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial. Clin Trials 2023; 20:661-669. [PMID: 37439089 PMCID: PMC10638852 DOI: 10.1177/17407745231186094] [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: 07/14/2023]
Abstract
BACKGROUND Recent work has shown that cluster-randomised trials can estimate two distinct estimands: the participant-average and cluster-average treatment effects. These can differ when participant outcomes or the treatment effect depends on the cluster size (termed informative cluster size). In this case, estimators that target one estimand (such as the analysis of unweighted cluster-level summaries, which targets the cluster-average effect) may be biased for the other. Furthermore, commonly used estimators such as mixed-effects models or generalised estimating equations with an exchangeable correlation structure can be biased for both estimands. However, there has been little empirical research into whether informative cluster size is likely to occur in practice. METHOD We re-analysed a cluster-randomised trial comparing two different thresholds for red blood cell transfusion in patients with acute upper gastrointestinal bleeding to explore whether estimates for the participant- and cluster-average effects differed, to provide empirical evidence for whether informative cluster size may be present. For each outcome, we first estimated a participant-average effect using independence estimating equations, which are unbiased under informative cluster size. We then compared this to two further methods: (1) a cluster-average effect estimated using either weighted independence estimating equations or unweighted cluster-level summaries, and (2) estimates from a mixed-effects model or generalised estimating equations with an exchangeable correlation structure. We then performed a small simulation study to evaluate whether observed differences between cluster- and participant-average estimates were likely to occur even if no informative cluster size was present. RESULTS For most outcomes, treatment effect estimates from different methods were similar. However, differences of >10% occurred between participant- and cluster-average estimates for 5 of 17 outcomes (29%). We also observed several notable differences between estimates from mixed-effects models or generalised estimating equations with an exchangeable correlation structure and those based on independence estimating equations. For example, for the EQ-5D VAS score, the independence estimating equation estimate of the participant-average difference was 4.15 (95% confidence interval: -3.37 to 11.66), compared with 2.84 (95% confidence interval: -7.37 to 13.04) for the cluster-average independence estimating equation estimate, and 3.23 (95% confidence interval: -6.70 to 13.16) from a mixed-effects model. Similarly, for thromboembolic/ischaemic events, the independence estimating equation estimate for the participant-average odds ratio was 0.43 (95% confidence interval: 0.07 to 2.48), compared with 0.33 (95% confidence interval: 0.06 to 1.77) from the cluster-average estimator. CONCLUSION In this re-analysis, we found that estimates from the various approaches could differ, which may be due to the presence of informative cluster size. Careful consideration of the estimand and the plausibility of assumptions underpinning each estimator can help ensure an appropriate analysis methods are used. Independence estimating equations and the analysis of cluster-level summaries (with appropriate weighting for each to correspond to either the participant-average or cluster-average treatment effect) are a desirable choice when informative cluster size is deemed possible, due to their unbiasedness in this setting.
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Affiliation(s)
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA
| | - Bryan Blette
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vipul Jairath
- Division of Gastroenterology, Department of Medicine, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Department of Epidemiology and Biostatistics, Western University, London, ON, Canada
| | | | - Michael Harhay
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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11
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Saing CH, Ung M, Suy S, Oy S, Dary C, Yam ELY, Chhorn S, Nagashima-Hayashi M, Khuon D, Mam S, Kim R, Saphonn V, Yi S. i-MoMCARE: Innovative Mobile Technology for Maternal and Child Health Care in Cambodia-study protocol of a cluster randomized controlled trial. Trials 2023; 24:692. [PMID: 37880782 PMCID: PMC10601211 DOI: 10.1186/s13063-023-07724-z] [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: 04/14/2023] [Accepted: 10/10/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The Government of Cambodia established the village health support groups (VHSGs) in 2003 to facilitate primary healthcare activities, including maternal and child health (MCH) services. However, VHSGs face several challenges that hinder them from performing optimally, including a lack of regular structured training and remuneration and limited and inconsistent support and supervision from the health centers (HCs). This implementation research aims to develop, implement, and evaluate a digital health intervention to improve the performance of VHSGs through better support and supervision and increase the MCH service coverage in rural Cambodia. METHODS i-MoMCARE, a two-arm cluster randomized controlled trial, will be conducted between 2022 and 2025. Five operational districts (ODs) have been randomized to an intervention arm and the other five ODs to the control arm. The intervention will last for 24 months. Around 200 VHSGs in the intervention arm will be equipped with a mobile application as a job aid and 20 HC staff with a web interface to improve support and supervision of VHSGs. The potential beneficiaries will include pregnant women, mothers, and children under 2 years old. We will measure the outcomes at baseline and endline. The primary outcomes will consist of a composite MCH index constructed from maternal and newborn care indicators, child immunization, and treatment of under-two children. Secondary outcomes will include coverage of selected MCH services. We will conduct the intention-to-treat and per-protocol analyses. We will conduct qualitative interviews with selected beneficiaries and stakeholders to evaluate the intervention's acceptability, feasibility, and scalability. We will also conduct a cost-effective analysis using decision-analytic modeling incorporating a societal perspective that explores different time horizons, intervention effects, and when scaled up to the national level. DISCUSSION i-MoMCARE is expected to increase MCH service access and coverage in rural Cambodia. It will contribute to advancing digital health use in primary healthcare interventions, which remains in its infancy in the country. Furthermore, the study findings will be a valuable addition to a growing body of literature on the effectiveness and feasibility of mobile health to improve coverage of MCH services in rural low- and middle-income country settings. TRIAL REGISTRATION ClinicalTrial.gov NCT05639595. Registered on 06 December 2022.
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Affiliation(s)
- Chan Hang Saing
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore
| | - Mengieng Ung
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore
| | | | - Sreymom Oy
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore
| | | | - Esabelle Lo Yan Yam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore
- College of Health and Medicine, Australian National University, Canberra, Australia
| | | | - Michiko Nagashima-Hayashi
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore
| | - Dyna Khuon
- University of Health Sciences, Phnom Penh, Cambodia
| | - Sovatha Mam
- University of Health Sciences, Phnom Penh, Cambodia
| | - Rattana Kim
- National Maternal and Child Health Center, Phnom Penh, Cambodia
| | | | - Siyan Yi
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore.
- KHANA Center for Population Health Research, Phnom Penh, Cambodia.
- Public Health Program, College of Education and Health Sciences, Touro University California, Vallejo, CA, USA.
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12
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Kaasalainen S, Wickson-Griffiths A, Hunter P, Thompson G, Kruizinga J, McCleary L, Sussman T, Venturato L, Shaw S, Boamah SA, Bourgeois-Guérin V, Hadjistavropoulos T, Macdonald M, Martin-Misener R, McClement S, Parker D, Penner J, Ploeg J, Sinclair S, Fisher K. Evaluation of the Strengthening a Palliative Approach in Long Term Care (SPA-LTC) programme: a protocol of a cluster randomised control trial. BMJ Open 2023; 13:e073585. [PMID: 37880170 PMCID: PMC10603462 DOI: 10.1136/bmjopen-2023-073585] [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: 03/10/2023] [Accepted: 09/28/2023] [Indexed: 10/27/2023] Open
Abstract
INTRODUCTION Despite the high mortality rates in long-term care (LTC) homes, most do not have a formalised palliative programme. Hence, our research team has developed the Strengthening a Palliative Approach in Long Term Care (SPA-LTC) programme. The goal of the proposed study is to examine the implementation and effectiveness of the SPA-LTC programme. METHODS AND ANALYSIS A cross-jurisdictional, effectiveness-implementation type II hybrid cluster randomised control trial design will be used to assess the SPA-LTC programme for 18 LTC homes (six homes within each of three provinces). Randomisation will occur at the level of the LTC home within each province, using a 1:1 ratio (three homes in the intervention and control groups). Baseline staff surveys will take place over a 3-month period at the beginning for both the intervention and control groups. The intervention group will then receive facilitated training and education for staff, and residents and their family members will participate in the SPA-LTC programme. Postintervention data collection will be conducted in a similar manner as in the baseline period for both groups. The overall target sample size will be 594 (297 per arm, 33 resident/family member participants per home, 18 homes). Data collection and analysis will involve organisational, staff, resident and family measures. The primary outcome will be a binary measure capturing any emergency department use in the last 6 months of life (resident); with secondary outcomes including location of death (resident), satisfaction and decisional conflict (family), knowledge and confidence implementing a palliative approach (staff), along with implementation outcomes (ie, feasibility, reach, fidelity and perceived sustainability of the SPA-LTC programme). The primary outcome will be analysed via multivariable logistic regression using generalised estimating equations. Intention-to-treat principles will be used in the analysis. ETHICS AND DISSEMINATION The study has received ethical approval. Results will be disseminated at various presentations and feedback sessions; at provincial, national and international conferences, and in a series of manuscripts that will be submitted to peer-reviewed, open access journals. TRIAL REGISTRATION NUMBER NCT039359.
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Affiliation(s)
- Sharon Kaasalainen
- School of Nursing, McMaster University, Hamilton, Ontario, Canada
- Gladys Sharpe Chair in Nursing, School of Nursing, McMaster University, Hamilton, Ontario, Canada
| | | | | | | | - Julia Kruizinga
- School of Nursing, McMaster University, Hamilton, Ontario, Canada
| | - Lynn McCleary
- Department of Nursing, Faculty of Applied Health Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Tamara Sussman
- School of Social Work, McGill University, Montreal, Québec, Canada
| | | | - Sally Shaw
- School of Nursing, McMaster University, Hamilton, Ontario, Canada
| | - Sheila A Boamah
- School of Nursing, McMaster University, Hamilton, Ontario, Canada
| | | | | | | | | | - Susan McClement
- College of Nursing, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Deborah Parker
- Aged Care, University of Technology Sydney Faculty of Health, Sydney, New South Wales, Australia
| | - Jamie Penner
- College of Nursing, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Jenny Ploeg
- School of Nursing, McMaster University, Hamilton, Ontario, Canada
| | - Shane Sinclair
- Faculty of Nursing, University of Calgary, Calgary, Alberta, Canada
| | - Kathryn Fisher
- School of Nursing, McMaster University, Hamilton, Ontario, Canada
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13
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Hemming K, Taljaard M. Key considerations for designing, conducting and analysing a cluster randomized trial. Int J Epidemiol 2023; 52:1648-1658. [PMID: 37203433 PMCID: PMC10555937 DOI: 10.1093/ije/dyad064] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 05/02/2023] [Indexed: 05/20/2023] Open
Abstract
Not only do cluster randomized trials require a larger sample size than individually randomized trials, they also face many additional complexities. The potential for contamination is the most commonly used justification for using cluster randomization, but the risk of contamination should be carefully weighed against the more serious problem of questionable scientific validity in settings with post-randomization identification or recruitment of participants unblinded to the treatment allocation. In this paper we provide some simple guidelines to help researchers conduct cluster trials in a way that minimizes potential biases and maximizes statistical efficiency. The overarching theme of this guidance is that methods that apply to individually randomized trials rarely apply to cluster randomized trials. We recommend that cluster randomization be only used when necessary-balancing the benefits of cluster randomization with its increased risks of bias and increased sample size. Researchers should also randomize at the lowest possible level-balancing the risks of contamination with ensuring an adequate number of randomization units-as well as exploring other options for statistically efficient designs. Clustering should always be allowed for in the sample size calculation; and the use of restricted randomization (and adjustment in the analysis for covariates used in the randomization) should be considered. Where possible, participants should be recruited before randomizing clusters and, when recruiting (or identifying) participants post-randomization, recruiters should be masked to the allocation. In the analysis, the target of inference should align with the research question, and adjustment for clustering and small sample corrections should be used when the trial includes less than about 40 clusters.
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Affiliation(s)
- Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - 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
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14
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Ryan MM, Esserman D, Li F. Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity. Stat Med 2023; 42:3764-3785. [PMID: 37339777 PMCID: PMC10510425 DOI: 10.1002/sim.9830] [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: 02/11/2022] [Revised: 04/19/2023] [Accepted: 05/29/2023] [Indexed: 06/22/2023]
Abstract
Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre-specified, hypothesis-driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed-form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre-specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome-ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes Prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters.
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Affiliation(s)
- Mary M. Ryan
- Department of Biostatistics, Yale School of Public Health, Connecticut, USA
- Yale Center for Analytical Sciences, Yale School of Public Health, Connecticut, USA
| | - Denise Esserman
- Department of Biostatistics, Yale School of Public Health, Connecticut, USA
- Yale Center for Analytical Sciences, Yale School of Public Health, Connecticut, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, Connecticut, USA
- Yale Center for Analytical Sciences, Yale School of Public Health, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, Connecticut, USA
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15
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Meng C, Ryan M, Rathouz PJ, Turner EL, Preisser JS, Li F. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 237:107567. [PMID: 37207384 DOI: 10.1016/j.cmpb.2023.107567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/31/2023] [Accepted: 04/21/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVES Marginal models with generalized estimating equations (GEE) are usually recommended for analyzing correlated ordinal outcomes which are commonly seen in a longitudinal study or clustered randomized trial (CRT). Within-cluster association is often of interest in longitudinal studies or CRTs, and can be estimated with paired estimating equations. However, the estimators for within-cluster association parameters and variances may be subject to finite-sample biases when the number of clusters is small. The objective of this article is to introduce a newly developed R package ORTH.Ord for analyzing correlated ordinal outcomes using GEE models with finite-sample bias corrections. METHODS The R package ORTH.Ord implements a modified version of alternating logistic regressions with estimation based on orthogonalized residuals (ORTH), which use paired estimating equations to jointly estimate parameters in marginal mean and association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). The R package also provides a finite-sample bias correction to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and bias-corrected sandwich estimators with different options for covariance estimation. RESULTS A simulation study shows that MMORTH provides less biased global POR estimates and coverage of their 95% confidence intervals closer to the nominal level than uncorrected ORTH. An analysis of patient-reported outcomes from an orthognathic surgery clinical trial illustrates features of ORTH.Ord. CONCLUSIONS This article provides an overview of the ORTH method with bias-correction on both estimating equations and sandwich estimators for analyzing correlated ordinal data, describes the features of the ORTH.Ord R package, evaluates the performance of the package using a simulation study, and finally illustrates its application in an analysis of a clinical trial.
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Affiliation(s)
- Can Meng
- Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, 06511, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, 06511, CT, USA.
| | - Mary Ryan
- Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, 06511, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, 06511, CT, USA
| | - Paul J Rathouz
- Department of Population Health, University of Texas at Austin, Austin, 78712, TX, USA
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, 27710, NC, USA
| | - John S Preisser
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC, USA
| | - Fan Li
- Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, 06511, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, 06511, CT, USA; Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, 06511, CT, USA
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16
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Ozturk O, Kravchuk O, Jarrett R. Models for cluster randomized designs using ranked set sampling. Stat Med 2023. [PMID: 37041108 DOI: 10.1002/sim.9743] [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: 10/21/2022] [Revised: 02/20/2023] [Accepted: 03/25/2023] [Indexed: 04/13/2023]
Abstract
Cluster randomized designs (CRD) provide a rigorous development for randomization principles for studies where treatments are allocated to cluster units rather than the individual subjects within clusters. It is known that CRDs are less efficient than completely randomized designs since the randomization of treatment allocation is applied to the cluster units. To mitigate this problem, we embed a ranked set sampling design from survey sampling studies into CRD for the selection of both cluster and subsampling units. We show that ranking groups in ranked set sampling act like a covariate, reduce the expected mean squared cluster error, and increase the precision of the sampling design. We provide an optimality result to determine the sample sizes at cluster and sub-sample level. We apply the proposed sampling design to a dental study on human tooth size, and to a longitudinal study from an education intervention program.
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Affiliation(s)
- Omer Ozturk
- Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, Ohio, 43210, USA
| | - Olena Kravchuk
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide, South Australia, Australia
| | - Richard Jarrett
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide, South Australia, Australia
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17
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Kahan BC, Li F, Copas AJ, Harhay MO. Estimands in cluster-randomized trials: choosing analyses that answer the right question. Int J Epidemiol 2023; 52:107-118. [PMID: 35834775 PMCID: PMC9908044 DOI: 10.1093/ije/dyac131] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 06/07/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Cluster-randomized trials (CRTs) involve randomizing groups of individuals (e.g. hospitals, schools or villages) to different interventions. Various approaches exist for analysing CRTs but there has been little discussion around the treatment effects (estimands) targeted by each. METHODS We describe the different estimands that can be addressed through CRTs and demonstrate how choices between different analytic approaches can impact the interpretation of results by fundamentally changing the question being asked, or, equivalently, the target estimand. RESULTS CRTs can address either the participant-average treatment effect (the average treatment effect across participants) or the cluster-average treatment effect (the average treatment effect across clusters). These two estimands can differ when participant outcomes or the treatment effect depends on the cluster size (referred to as 'informative cluster size'), which can occur for reasons such as differences in staffing levels or types of participants between small and large clusters. Furthermore, common estimators, such as mixed-effects models or generalized estimating equations with an exchangeable working correlation structure, can produce biased estimates for both the participant-average and cluster-average treatment effects when cluster size is informative. We describe alternative estimators (independence estimating equations and cluster-level analyses) that are unbiased for CRTs even when informative cluster size is present. CONCLUSION We conclude that careful specification of the estimand at the outset can ensure that the study question being addressed is clear and relevant, and, in turn, that the selected estimator provides an unbiased estimate of the desired quantity.
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Affiliation(s)
- Brennan C Kahan
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, London, UK
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, CT, USA
| | - Andrew J Copas
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, London, UK
| | - Michael O Harhay
- Clinical Trials Methods and Outcomes Lab, PAIR (Palliative and Advanced Illness Research) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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18
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Meng C, Esserman D, Li F, Zhao Y, Blaha O, Lu W, Wang Y, Peduzzi P, Greene EJ. Simulating time-to-event data subject to competing risks and clustering: A review and synthesis. Stat Methods Med Res 2023; 32:305-333. [PMID: 36412111 DOI: 10.1177/09622802221136067] [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: 11/23/2022]
Abstract
Simulation studies play an important role in evaluating the performance of statistical models developed for analyzing complex survival data such as those with competing risks and clustering. This article aims to provide researchers with a basic understanding of competing risks data generation, techniques for inducing cluster-level correlation, and ways to combine them together in simulation studies, in the context of randomized clinical trials with a binary exposure or treatment. We review data generation with competing and semi-competing risks and three approaches of inducing cluster-level correlation for time-to-event data: the frailty model framework, the probability transform, and Moran's algorithm. Using exponentially distributed event times as an example, we discuss how to introduce cluster-level correlation into generating complex survival outcomes, and illustrate multiple ways of combining these methods to simulate clustered, competing and semi-competing risks data with pre-specified correlation values or degree of clustering.
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Affiliation(s)
- Can Meng
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA.,Yale Center for Analytical Sciences, New Haven, CT USA
| | - Denise Esserman
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA.,Yale Center for Analytical Sciences, New Haven, CT USA
| | - Fan Li
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA.,Yale Center for Analytical Sciences, New Haven, CT USA
| | - Yize Zhao
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA.,Yale Center for Analytical Sciences, New Haven, CT USA
| | - Ondrej Blaha
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA.,Yale Center for Analytical Sciences, New Haven, CT USA
| | - Wenhan Lu
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA
| | - Yuxuan Wang
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA
| | - Peter Peduzzi
- Department of Biostatistics, 50296Yale University School of Public Health, New Haven, CT USA.,Yale Center for Analytical Sciences, New Haven, CT USA
| | - Erich J Greene
- Department of Biostatistics, 50296Yale 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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Vaughan R. Embracing Advanced Methodology to Improve Population Health. Am J Public Health 2023; 113:35-36. [PMID: 36516382 PMCID: PMC9755939 DOI: 10.2105/ajph.2022.307155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Roger Vaughan
- Roger Vaughan is an associate editor for AJPH and is with the Department of Biostatistics at The Rockefeller University Hospital, New York, NY
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20
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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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21
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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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22
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Li F, Tian Z, Tian Z, Li F. A note on identification of causal effects in cluster randomized trials with post-randomization selection bias. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2116281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Zizhong Tian
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University, Hershey, Pennsylvania, USA
| | - Zibo Tian
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Fan Li
- Department of Statistical Science, Duke University, Durham, North Carolina, USA
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Szefler SJ, Cicutto L, Brewer SE, Gleason M, McFarlane A, DeCamp LR, Brinton JT, Huebschmann AG. Applying dissemination and implementation research methods to translate a school-based asthma program. J Allergy Clin Immunol 2022; 150:535-548. [PMID: 35569568 PMCID: PMC9590448 DOI: 10.1016/j.jaci.2022.04.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 11/23/2022]
Abstract
Our school-based asthma program has reduced asthma exacerbations for youth with health disparities in the Denver metropolitan area, due partly to addressing social determinants of health, such as access to health care and medications. Dissemination and implementation (D&I) science approaches accelerate the translation of evidence-based programs into routine practice. D&I methods are being applied more commonly to improve health equity. The purpose of this publication was to give an overview of D&I research methods, using our school-based asthma program as an example. To successfully scale out our program across the state of Colorado, we are applying a D&I framework that guides the adaptation of our existing implementation approach to better meet our stakeholders' local context-the Exploration, Preparation, Implementation, Sustainment framework. In a pragmatic trial design, we will evaluate the outcomes of implementing the program across 5 Colorado regions, with attention to health equity, using a second commonly used D&I framework-Reach, Effectiveness, Adoption, Implementation, and Maintenance. Our central hypothesis is that our program will have broad and equitable reach to eligible students (primary outcome) and will reduce asthma attacks and symptoms. This D&I approach accelerates dissemination of our program and is an applicable process for translating other effective allergy/asthma programs to address asthma and allergy-related disparities.
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Affiliation(s)
- Stanley J Szefler
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Children's Hospital Colorado Breathing Institute, Aurora, Colo; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo.
| | - Lisa Cicutto
- National Jewish Health, Aurora, Colo; Clinical Science and College of Nursing, University of Colorado, Anschutz Medical Campus, Aurora, Colo
| | - Sarah E Brewer
- Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo; Department of Family Medicine, University of Colorado, Anschutz Medical Campus, Aurora, Colo
| | - Melanie Gleason
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Children's Hospital Colorado Breathing Institute, Aurora, Colo
| | | | - Lisa Ross DeCamp
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo; Latino Research and Policy Center, Colorado School of Public Health, Aurora, Colo
| | - John T Brinton
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Children's Hospital Colorado Breathing Institute, Aurora, Colo
| | - Amy G Huebschmann
- Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo; Department of Medicine, Division of General Internal Medicine, University of Colorado, Aurora, Colo; Ludeman Family Center for Women's Health Research, Aurora, Colo
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Hemming K, Taljaard M. Commentary: Estimands in cluster trials: thinking carefully about the target of inferenceand the consequences for analysis choice. Int J Epidemiol 2022; 52:116-118. [PMID: 36018244 PMCID: PMC9908041 DOI: 10.1093/ije/dyac174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/17/2022] [Indexed: 11/14/2022] Open
Affiliation(s)
- Karla Hemming
- Corresponding author. Institute of Applied Health Research, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. E-mail:
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada,School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Canada
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Couples-based interventions and postpartum contraceptive uptake: A systematic review. Contraception 2022; 112:23-36. [PMID: 35577147 PMCID: PMC9968552 DOI: 10.1016/j.contraception.2022.05.001] [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: 09/23/2021] [Revised: 04/29/2022] [Accepted: 05/02/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Systematically review the existing evidence about couples-based interventions and postpartum contraceptive uptake and generate recommendations for future research. DATA SOURCES PubMed, Web of Science, PsycINFO, Embase, and CINAHL through June 7, 2021. STUDY SELECTION AND DATA EXTRACTION Studies with a couples-based intervention assessing postpartum contraceptive uptake. Two independent reviewers screened studies, extracted data, and assessed risk of bias with RoB-2 (Cochrane Risk of Bias 2) for randomized and ROBINS-I (Risk of Bias in Non-Randomized Studies - Interventions) for observational studies. Data were synthesized in tables, figures, and a narrative review. RESULTS A total of 925 papers were identified, 66 underwent full text review, and 17 articles, which included 18 studies - 16 randomized, 2 observational - were included. The lack of intervention and outcome homogeneity precluded meta-analysis and isolating the effect of partner involvement. Four studies were partner-required, where partner involvement was a required component of the intervention, and 14 were partner-optional. Unadjusted risk differences ranged from 0.01 to 0.51 in favor of couples-based interventions increasing postpartum contraceptive uptake versus standard of care. Bias assessment of the 16 randomized studies classified 8, 3, and 5 studies as at a high, some concern, and low risk of bias. Common sources of bias included intervention non-adherence and missing outcome data. One observational study was at a high and the other at a low risk of bias. CONCLUSIONS Future studies that assess couples-based interventions must clearly define and measure how partners are involved in the intervention and assess how intervention adherence impacts postpartum contraceptive uptake.
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Li F, Yu H, Rathouz PJ, Turner EL, Preisser JS. Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes. Biostatistics 2022; 23:772-788. [PMID: 33527999 PMCID: PMC9291643 DOI: 10.1093/biostatistics/kxaa056] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 11/30/2020] [Indexed: 01/09/2023] Open
Abstract
Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations, but the joint estimation of the mean and intraclass correlation coefficients (ICCs) can be computationally intensive due to large cluster-period sizes. Motivated by the need for marginal inference in SW-CRTs, we propose a simple and efficient estimating equations approach to analyze cluster-period means. We show that the quasi-score for the marginal mean defined from individual-level observations can be reformulated as the quasi-score for the same marginal mean defined from the cluster-period means. An additional mapping of the individual-level ICCs into correlations for the cluster-period means further provides a rigorous justification for the cluster-period approach. The proposed approach addresses a long-recognized computational burden associated with estimating equations defined based on individual-level observations, and enables fast point and interval estimation of the intervention effect and correlations. We further propose matrix-adjusted estimating equations to improve the finite-sample inference for ICCs. By providing a valid approach to estimate ICCs within the class of generalized linear models for correlated binary outcomes, this article operationalizes key recommendations from the CONSORT extension to SW-CRTs, including the reporting of ICCs.
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Affiliation(s)
- Fan Li
- To whom correspondence should be addressed.
| | - Hengshi Yu
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Paul J Rathouz
- Department of Population Health, The University of Texas at Austin, Dell Medical School, 1601 Trinity St, Bldg. B, Austin, TX 78712, USA
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, 2424 Erwin Rd, Durham, NC 27710, USA
| | - John S Preisser
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27514, USA
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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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Purtle J, Nelson KL, Gebrekristos L, Lê-Scherban F, Gollust SE. Partisan differences in the effects of economic evidence and local data on legislator engagement with dissemination materials about behavioral health: a dissemination trial. Implement Sci 2022; 17:38. [PMID: 35729630 PMCID: PMC9213102 DOI: 10.1186/s13012-022-01214-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND State legislators make policy decisions that influence children's exposure to adverse childhood experiences (ACEs), such as child maltreatment, and their effects on behavioral health. Effective dissemination of scientific research can increase the likelihood that legislators' decisions are aligned with evidence to prevent ACEs and their consequences, and effective dissemination requires legislators to engage with dissemination materials. Informed by the elaboration likelihood model of persuasive communication and Brownson's Model of Dissemination Research, we tested the hypothesis that inclusion of economic evidence and local data would increase legislator engagement with dissemination materials about evidence-supported policies related to ACEs and behavioral health. METHODS A three-arm randomized dissemination trial was conducted. A university researcher e-mailed dissemination materials which contained evidence about ACEs and behavioral health problems to state legislators (two e-mails sent 2 weeks apart, 12,662 e-mails delivered to 6509 legislators). The e-mail subject lines, text, and policy brief content were manipulated across the study arms. The intervention condition received state-tailored data about rates of ACEs and state-tailored economic evidence about the costs of ACEs for public systems, the enhanced control condition received state-tailored data and not economic evidence, and the control condition received national data and not economic evidence. Outcomes were rates of e-mail views, policy brief link clicks, requests for researcher consultation, and mentions of child maltreatment terms in legislators' social media posts. RESULTS For the first e-mail, the e-mail view rate was 42.6% higher in the intervention than in the enhanced control condition (22.8% vs. 14.8%) and 20.8% higher than in the control condition (22.8% vs. 18.5%) (both p < .0001). Similar results were observed for the second e-mail. These differences remained significant after adjustment for demographic differences across study conditions in individual-level models, but not multilevel models. There was a significant interaction between the experimental condition and political party (p < .0001) in which the intervention increased e-mail view rates among Democrats but not Republicans. The intervention had no effect on policy brief link clicks or requests for consultation and a mixed effect on social media posts. CONCLUSIONS Inclusion of state-tailored economic evidence in dissemination materials can increase engagement with research evidence among Democrat, but not Republican, legislators. Dissemination strategies tailored for legislators' political party affiliation may be needed.
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Affiliation(s)
- Jonathan Purtle
- New York University School of Global Public Health, New York City, USA.
| | | | | | | | - Sarah E Gollust
- University of Minnesota School of Public Health, Minneapolis, USA
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Kohrt BA, Turner EL, Gurung D, Wang X, Neupane M, Luitel NP, Kartha MR, Poudyal A, Singh R, Rai S, Baral PP, McCutchan S, Gronholm PC, Hanlon C, Lempp H, Lund C, Thornicroft G, Gautam K, Jordans MJD. Implementation strategy in collaboration with people with lived experience of mental illness to reduce stigma among primary care providers in Nepal (RESHAPE): protocol for a type 3 hybrid implementation effectiveness cluster randomized controlled trial. Implement Sci 2022; 17:39. [PMID: 35710491 PMCID: PMC9205129 DOI: 10.1186/s13012-022-01202-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/10/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND There are increasing efforts for the integration of mental health services into primary care settings in low- and middle-income countries. However, commonly used approaches to train primary care providers (PCPs) may not achieve the expected outcomes for improved service delivery, as evidenced by low detection rates of mental illnesses after training. One contributor to this shortcoming is the stigma among PCPs. Implementation strategies for training PCPs that reduce stigma have the potential to improve the quality of services. DESIGN In Nepal, a type 3 hybrid implementation-effectiveness cluster randomized controlled trial will evaluate the implementation-as-usual training for PCPs compared to an alternative implementation strategy to train PCPs, entitled Reducing Stigma among Healthcare Providers (RESHAPE). In implementation-as-usual, PCPs are trained on the World Health Organization Mental Health Gap Action Program Intervention Guide (mhGAP-IG) with trainings conducted by mental health specialists. In RESHAPE, mhGAP-IG training includes the added component of facilitation by people with lived experience of mental illness (PWLE) and their caregivers using PhotoVoice, as well as aspirational figures. The duration of PCP training is the same in both arms. Co-primary outcomes of the study are stigma among PCPs, as measured with the Social Distance Scale at 6 months post-training, and reach, a domain from the RE-AIM implementation science framework. Reach is operationalized as the accuracy of detection of mental illness in primary care facilities and will be determined by psychiatrists at 3 months after PCPs diagnose the patients. Stigma will be evaluated as a mediator of reach. Cost-effectiveness and other RE-AIM outcomes will be assessed. Twenty-four municipalities, the unit of clustering, will be randomized to either mhGAP-IG implementation-as-usual or RESHAPE arms, with approximately 76 health facilities and 216 PCPs divided equally between arms. An estimated 1100 patients will be enrolled for the evaluation of accurate diagnosis of depression, generalized anxiety disorder, psychosis, or alcohol use disorder. Masking will include PCPs, patients, and psychiatrists. DISCUSSION This study will advance the knowledge of stigma reduction for training PCPs in partnership with PWLE. This collaborative approach to training has the potential to improve diagnostic competencies. If successful, this implementation strategy could be scaled up throughout low-resource settings to reduce the global treatment gap for mental illness. TRIAL REGISTRATION ClinicalTrials.gov, NCT04282915 . Date of registration: February 25, 2020.
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Affiliation(s)
- Brandon A. Kohrt
- grid.253615.60000 0004 1936 9510Division of Global Mental Health, Department of Psychiatry, George Washington University, Washington D.C., USA
| | - Elizabeth L. Turner
- grid.26009.3d0000 0004 1936 7961Department of Biostatistics and Bioinformatics and Duke Global Health Institute, Duke University, Durham, NC USA
| | - Dristy Gurung
- Transcultural Psychosocial Organization Nepal (TPO Nepal), Pokhara, Nepal
| | - Xueqi Wang
- grid.26009.3d0000 0004 1936 7961Department of Biostatistics and Bioinformatics and Duke Global Health Institute, Duke University, Durham, NC USA
| | - Mani Neupane
- Transcultural Psychosocial Organization Nepal (TPO Nepal), Pokhara, Nepal
| | - Nagendra P. Luitel
- Transcultural Psychosocial Organization Nepal (TPO Nepal), Kathmandu, Nepal
| | - Muralikrishnan R. Kartha
- grid.13097.3c0000 0001 2322 6764King’s Health Economics, IOPPN, King’s College London, London, UK
| | - Anubhuti Poudyal
- grid.21729.3f0000000419368729Department of Sociomedical Sciences, Columbia University, New York, NY USA ,grid.253615.60000 0004 1936 9510Division of Global Mental Health, Department of Psychiatry, George Washington University, Washington, D.C., 20036 USA
| | - Ritika Singh
- grid.253615.60000 0004 1936 9510Division of Global Mental Health, Department of Psychiatry, George Washington University, Washington, D.C., 20036 USA
| | - Sauharda Rai
- grid.34477.330000000122986657Jackson School of International Studies and Department of Global Health, University of Washington, Seattle, USA
| | - Phanindra Prasad Baral
- grid.500537.4Non-communicable Disease and Mental Health Section, Epidemiology and Disease Control Division (EDCD), Department of Health Services (DoHS), Ministry of Health and Population (MoHP), Kathmandu, Nepal
| | - Sabrina McCutchan
- grid.26009.3d0000 0004 1936 7961Duke Global Health Institute, Duke University, Durham, NC USA
| | - Petra C. Gronholm
- grid.13097.3c0000 0001 2322 6764Centre for Global Mental Health and Centre for Implementation Science, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Charlotte Hanlon
- grid.13097.3c0000 0001 2322 6764Centre for Global Mental Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK ,grid.7123.70000 0001 1250 5688Department of Psychiatry, School of Medicine and Centre for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Heidi Lempp
- grid.13097.3c0000 0001 2322 6764Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK
| | - Crick Lund
- grid.13097.3c0000 0001 2322 6764Centre for Global Mental Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK ,grid.7836.a0000 0004 1937 1151Alan J Flisher Centre for Public Mental Health, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Graham Thornicroft
- grid.13097.3c0000 0001 2322 6764Centre for Global Mental Health and Centre for Implementation Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Kamal Gautam
- Transcultural Psychosocial Organization Nepal (TPO Nepal), Kathmandu, Nepal
| | - Mark J. D. Jordans
- grid.13097.3c0000 0001 2322 6764Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, Center for Global Mental Health, King’s College London, London, UK
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Ficek J, Chen H, Lu Y, Huang Y, Mayer JM. Assessing the impacts of cluster effects and covariate imbalance in cluster randomized equivalence trials. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2071981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Joseph Ficek
- College of Public Health, University of South Florida (USF), Tampa, FL 33612, USA
| | - Henian Chen
- College of Public Health, University of South Florida (USF), Tampa, FL 33612, USA
| | - Yuanyuan Lu
- College of Public Health, University of South Florida (USF), Tampa, FL 33612, USA
| | - Yangxin Huang
- College of Public Health, University of South Florida (USF), Tampa, FL 33612, USA
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Stepped Wedge Cluster Randomized Trials: A Methodological Overview. World Neurosurg 2022; 161:323-330. [PMID: 35505551 PMCID: PMC9074087 DOI: 10.1016/j.wneu.2021.10.136] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 11/21/2022]
Abstract
BACKGROUND Stepped wedge cluster randomized trials enable rigorous evaluations of health intervention programs in pragmatic settings. In the present study, we aimed to update neurosurgeon scientists on the design of stepped wedge randomized trials. METHODS We have presented an overview of recent methodological developments for stepped wedge designs and included an update on the newer associated methodological tools to aid with future study designs. RESULTS We defined the stepped wedge trial design and reviewed the indications for the design in depth. In addition, key considerations, including mainstream methods of analysis and sample size determination, were discussed. CONCLUSIONS Stepped wedge designs can be attractive for study intervention programs aiming to improve the delivery of patient care, especially when examining a small number of heterogeneous clusters.
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Westgate PM, Cheng DM, Feaster DJ, Fernández S, Shoben AB, Vandergrift N. Marginal modeling in community randomized trials with rare events: Utilization of the negative binomial regression model. Clin Trials 2022; 19:162-171. [PMID: 34991359 PMCID: PMC9038610 DOI: 10.1177/17407745211063479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND/AIMS This work is motivated by the HEALing Communities Study, which is a post-test only cluster randomized trial in which communities are randomized to two different trial arms. The primary interest is in reducing opioid overdose fatalities, which will be collected as a count outcome at the community level. Communities range in size from thousands to over one million residents, and fatalities are expected to be rare. Traditional marginal modeling approaches in the cluster randomized trial literature include the use of generalized estimating equations with an exchangeable correlation structure when utilizing subject-level data, or analogously quasi-likelihood based on an over-dispersed binomial variance when utilizing community-level data. These approaches account for and estimate the intra-cluster correlation coefficient, which should be provided in the results from a cluster randomized trial. Alternatively, the coefficient of variation or R coefficient could be reported. In this article, we show that negative binomial regression can also be utilized when communities are large and events are rare. The objectives of this article are (1) to show that the negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model and to explain why the estimates may differ; (2) to derive formulas relating the negative binomial overdispersion parameter k with the intra-cluster correlation coefficient, coefficient of variation, and R coefficient; and (3) analyze pre-intervention data from the HEALing Communities Study to demonstrate and contrast models and to show how to report the intra-cluster correlation coefficient, coefficient of variation, and R coefficient when utilizing negative binomial regression. METHODS Negative binomial and over-dispersed binomial regression modeling are contrasted in terms of model setup, regression parameter estimation, and formulation of the overdispersion parameter. Three specific models are used to illustrate concepts and address the third objective. RESULTS The negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model, although estimates may differ. Practical differences arise in regard to how overdispersion, and hence the intra-cluster correlation coefficient is modeled. The negative binomial overdispersion parameter is approximately equal to the ratio of the intra-cluster correlation coefficient and marginal probability, the square of the coefficient of variation, and the R coefficient minus 1. As a result, estimates corresponding to all four of these different types of overdispersion parameterizations can be reported when utilizing negative binomial regression. CONCLUSION Negative binomial regression provides a valid, practical, alternative approach to the analysis of count data, and corresponding reporting of overdispersion parameters, from community randomized trials in which communities are large and events are rare.
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Affiliation(s)
- Philip M Westgate
- Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Debbie M Cheng
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Daniel J Feaster
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Coral Gables, FL, USA
| | - Soledad Fernández
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Abigail B Shoben
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
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Wagner GJ, Bogart LM, Green HD, Storholm ED, Klein DJ, McBain RK, Serunkuuma R, Mubiru K, Matovu JKB, Okoboi S. Social network-based group intervention to promote HIV prevention in Uganda: study protocol for a cluster randomized controlled trial of Game Changers. Trials 2022; 23:233. [PMID: 35346329 PMCID: PMC8961890 DOI: 10.1186/s13063-022-06186-z] [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: 02/22/2022] [Accepted: 03/18/2022] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Innovative strategies are needed to disseminate HIV prevention messages across communities efficiently, as well as reduce HIV stigma while promoting HIV prevention. This randomized controlled trial will evaluate the efficacy of a social network-based group intervention, Game Changers, which trains persons living with HIV (PLWH) to encourage members of their social network to use HIV protective behaviors METHODS: PLWH in HIV care for at least 1 year will be randomly assigned to receive the 8-session group advocacy training intervention or no-intervention control group. Each enrolled PLWH (index participant) will be asked to recruit up to four social network members (alter participant). Assessments will be administered at baseline and months 6, 12, and 18 to both index and alter participants. The primary outcomes are HIV testing and condom use among alter participants; secondary outcomes are engagement in HIV prevention advocacy and internalized HIV stigma among index participants. Repeated-measures multivariable regression analyses will be conducted to compare outcomes between the intervention and control arms, in addition to a cost-effectiveness evaluation. DISCUSSION This social network-based approach to HIV prevention is particularly timely in the era of biomedical interventions, which require widespread penetration of effective HIV prevention and care messaging into communities. Positioning PLWH as central to the solution for controlling (vs. causing) the HIV epidemic has the potential to reduce HIV stigma and improve prevention outcomes at the individual and network levels. TRIAL REGISTRATION ClinicalTrials.gov NIH Clinical Trial Registry NCT05098015. Registered on October 18, 2021.
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Affiliation(s)
- Glenn J. Wagner
- grid.34474.300000 0004 0370 7685RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407-2138 USA
| | - Laura M. Bogart
- grid.34474.300000 0004 0370 7685RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407-2138 USA
| | - Harold D. Green
- grid.411377.70000 0001 0790 959XIndiana University School of Public Health, Bloomington, IN USA
| | - Erik D. Storholm
- grid.263081.e0000 0001 0790 1491San Diego State University, San Diego, CA USA
| | - David J. Klein
- grid.34474.300000 0004 0370 7685RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407-2138 USA
| | - Ryan K. McBain
- grid.34474.300000 0004 0370 7685RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407-2138 USA
| | - Richard Serunkuuma
- grid.509241.bInfectious Diseases Institute, College of Health Sciences, National Forum of People Living with HIV/AIDS Networks in Uganda, Kampala, Uganda
| | - Kuraish Mubiru
- grid.509241.bInfectious Diseases Institute, College of Health Sciences, National Forum of People Living with HIV/AIDS Networks in Uganda, Kampala, Uganda
| | - Joseph K. B. Matovu
- grid.11194.3c0000 0004 0620 0548School of Public Health, Makerere University, Kampala, Uganda ,grid.448602.c0000 0004 0367 1045Busitema University Faculty of Health Sciences, Mbale, Uganda
| | - Stephen Okoboi
- grid.509241.bInfectious Diseases Institute, College of Health Sciences, Kampala, Uganda
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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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35
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Fang Y, He W. Practical considerations in utilizing cluster randomized controlled trials conducted in biopharmaceutical industry. Clin Trials 2022; 19:416-421. [DOI: 10.1177/17407745211073484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cluster randomized controlled trials (cluster RCTs), also known as parallel-arm group-randomized trials, are trials in which the randomized units are groups of participants, as opposed to individual participants. These trials have largely been implemented to address broad public health issues, but with the growing interest in use of real-world data in the regulatory setting, this design may be increasingly considered for industry trials. The key difference between cluster RCTs and traditional RCTs is the intraclass correlation coefficient (ICC) that needs to be considered in cluster RCTs. In this article, we discuss some key practical considerations that are related to ICC in the design, conduct, analysis, and report stages of a cluster RCT. These key considerations related to ICC can lead to improvement in how we translate research findings from cluster RCTs into practices in the biopharmaceutical industry.
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36
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Jamshidi-Naeini Y, Brown AW, Mehta T, Glueck DH, Golzarri-Arroyo L, Muller KE, Tekwe CD, Allison DB. A practical decision tree to support editorial adjudication of submitted parallel cluster randomized controlled trials. Obesity (Silver Spring) 2022; 30:565-570. [PMID: 35195364 PMCID: PMC9203170 DOI: 10.1002/oby.23373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/08/2021] [Accepted: 12/12/2021] [Indexed: 01/23/2023]
Affiliation(s)
- Yasaman Jamshidi-Naeini
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Andrew W. Brown
- Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Tapan Mehta
- Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Deborah H. Glueck
- Department of Pediatrics, University of Colorado School of Medicine, University of Colorado Denver, Aurora, Colorado, USA
| | - Lilian Golzarri-Arroyo
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Keith E. Muller
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Carmen D. Tekwe
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - David B. Allison
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
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37
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Tian Z, Esserman D, Tong G, Blaha O, Dziura J, Peduzzi P, Li F. Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes. Stat Med 2022; 41:645-664. [PMID: 34978097 PMCID: PMC8962918 DOI: 10.1002/sim.9284] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 11/08/2022]
Abstract
Motivated by a suicide prevention trial with hierarchical treatment allocation (cluster-level and individual-level treatments), we address the sample size requirements for testing the treatment effects as well as their interaction. We assume a linear mixed model, within which two types of treatment effect estimands (controlled effect and marginal effect) are defined. For each null hypothesis corresponding to an estimand, we derive sample size formulas based on large-sample z-approximation, and provide finite-sample modifications based on a t-approximation. We relax the equal cluster size assumption and express the sample size formulas as functions of the mean and coefficient of variation of cluster sizes. We show that the sample size requirement for testing the controlled effect of the cluster-level treatment is more sensitive to cluster size variability than that for testing the controlled effect of the individual-level treatment; the same observation holds for testing the marginal effects. In addition, we show that the sample size for testing the interaction effect is proportional to that for testing the controlled or the marginal effect of the individual-level treatment. We conduct extensive simulations to validate the proposed sample size formulas, and find the empirical power agrees well with the predicted power for each test. Furthermore, the t-approximations often provide better control of type I error rate with a small number of clusters. Finally, we illustrate our sample size formulas to design the motivating suicide prevention factorial trial. The proposed methods are implemented in the R package H2x2Factorial.
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Affiliation(s)
- Zizhong Tian
- Department of Biostatistics, Yale University School of Public Health, Connecticut, USA
| | - Denise Esserman
- Department of Biostatistics, Yale University School of Public Health, Connecticut, USA,Yale Center for Analytical Sciences, Yale University, Connecticut, USA
| | - Guangyu Tong
- Department of Biostatistics, Yale University School of Public Health, Connecticut, USA,Yale Center for Analytical Sciences, Yale University, Connecticut, USA
| | - Ondrej Blaha
- Department of Biostatistics, Yale University School of Public Health, Connecticut, USA,Yale Center for Analytical Sciences, Yale University, Connecticut, USA
| | - James Dziura
- Department of Biostatistics, Yale University School of Public Health, Connecticut, USA,Yale Center for Analytical Sciences, Yale University, Connecticut, USA
| | - Peter Peduzzi
- Department of Biostatistics, Yale University School of Public Health, Connecticut, USA,Yale Center for Analytical Sciences, Yale University, Connecticut, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, Connecticut, USA,Yale Center for Analytical Sciences, Yale University, Connecticut, USA,Center for Methods in Implementation and Prevention Science, Yale University, Connecticut, USA,Correspondence Fan Li, PhD, Department of Biostatistics, Yale School of Public Health, New Haven CT, 06510, USA,
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38
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Zhou Y, Turner EL, Simmons RA, Li F. Constrained randomization and statistical inference for multi‐arm parallel cluster randomized controlled trials. Stat Med 2022; 41:1862-1883. [PMID: 35146788 PMCID: PMC9007899 DOI: 10.1002/sim.9333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 12/17/2022]
Abstract
A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi-arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi-arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs in multi-arm cRCTs, with varying combinations of design and analysis-based covariate adjustment strategies. In particular, as randomization-based tests have not been extensively studied in multi-arm cRCTs, we additionally develop most-powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model-based and randomization-based analyses could gain power while preserving nominal type I error rate, given proper analysis-based adjustment for the baseline covariates. Randomization-based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi-arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space.
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Affiliation(s)
- Yunji Zhou
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Ryan A. Simmons
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Fan Li
- Department of Biostatistics Yale School of Public Health New Haven Connecticut USA
- Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven Connecticut USA
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Abstract
BACKGROUND This article identifies the most influential methods reports for group-randomized trials and related designs published through 2020. Many interventions are delivered to participants in real or virtual groups or in groups defined by a shared interventionist so that there is an expectation for positive correlation among observations taken on participants in the same group. These interventions are typically evaluated using a group- or cluster-randomized trial, an individually randomized group treatment trial, or a stepped wedge group- or cluster-randomized trial. These trials face methodological issues beyond those encountered in the more familiar individually randomized controlled trial. METHODS PubMed was searched to identify candidate methods reports; that search was supplemented by reports known to the author. Candidate reports were reviewed by the author to include only those focused on the designs of interest. Citation counts and the relative citation ratio, a new bibliometric tool developed at the National Institutes of Health, were used to identify influential reports. The relative citation ratio measures influence at the article level by comparing the citation rate of the reference article to the citation rates of the articles cited by other articles that also cite the reference article. RESULTS In total, 1043 reports were identified that were published through 2020. However, 55 were deemed to be the most influential based on their relative citation ratio or their citation count using criteria specific to each of the three designs, with 32 group-randomized trial reports, 7 individually randomized group treatment trial reports, and 16 stepped wedge group-randomized trial reports. Many of the influential reports were early publications that drew attention to the issues that distinguish these designs from the more familiar individually randomized controlled trial. Others were textbooks that covered a wide range of issues for these designs. Others were "first reports" on analytic methods appropriate for a specific type of data (e.g. binary data, ordinal data), for features commonly encountered in these studies (e.g. unequal cluster size, attrition), or for important variations in study design (e.g. repeated measures, cohort versus cross-section). Many presented methods for sample size calculations. Others described how these designs could be applied to a new area (e.g. dissemination and implementation research). Among the reports with the highest relative citation ratios were the CONSORT statements for each design. CONCLUSIONS Collectively, the influential reports address topics of great interest to investigators who might consider using one of these designs and need guidance on selecting the most appropriate design for their research question and on the best methods for design, analysis, and sample size.
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Affiliation(s)
- David M Murray
- Office of Disease Prevention, National Institutes of Health, North Bethesda, MD, USA
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40
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Yu H, Tong G, Li F. A note on the estimation and inference with quadratic inference functions for correlated outcomes. COMMUN STAT-SIMUL C 2022; 51:6525-6536. [PMID: 36568127 PMCID: PMC9782733 DOI: 10.1080/03610918.2020.1805463] [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: 01/03/2023]
Abstract
The quadratic inference function approach is a popular method in the analysis of correlated data. The quadratic inference function is formulated based on multiple sets of score equations (or extended score equations) that over-identify the regression parameters of interest, and improves efficiency over the generalized estimating equations under correlation misspecification. In this note, we provide an alternative solution to the quadratic inference function by separately solving each set of score equations and combining the solutions. We provide an insight that an optimally weighted combination of estimators obtained separately from the distinct sets of score equations is asymptotically equivalent to the estimator obtained via the quadratic inference function. We further establish results on inference for the optimally weighted estimator and extend these insights to the general setting with over-identified estimating equations. A simulation study is carried out to confirm the analytical insights and connections in finite samples.
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Affiliation(s)
- Hengshi Yu
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
| | - Guangyu Tong
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, U.S.A.
| | - Fan Li
- Department of Biostatistics, Yale University, New Haven, Connecticut, U.S.A
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41
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Wang W, Harhay MO. A comparative study of R functions for clustered data analysis. Trials 2021; 22:959. [PMID: 34961539 PMCID: PMC8711156 DOI: 10.1186/s13063-021-05900-7] [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: 11/27/2020] [Accepted: 12/01/2021] [Indexed: 08/26/2023] Open
Abstract
Background Clustered or correlated outcome data is common in medical research studies, such as the analysis of national or international disease registries, or cluster-randomized trials, where groups of trial participants, instead of each trial participant, are randomized to interventions. Within-group correlation in studies with clustered data requires the use of specific statistical methods, such as generalized estimating equations and mixed-effects models, to account for this correlation and support unbiased statistical inference. Methods We compare different approaches to estimating generalized estimating equations and mixed effects models for a continuous outcome in R through a simulation study and a data example. The methods are implemented through four popular functions of the statistical software R, “geese”, “gls”, “lme”, and “lmer”. In the simulation study, we compare the mean squared error of estimating all the model parameters and compare the coverage proportion of the 95% confidence intervals. In the data analysis, we compare estimation of the intervention effect and the intra-class correlation. Results In the simulation study, the function “lme” takes the least computation time. There is no difference in the mean squared error of the four functions. The “lmer” function provides better coverage of the fixed effects when the number of clusters is small as 10. The function “gls” produces close to nominal scale confidence intervals of the intra-class correlation. In the data analysis and the “gls” function yields a positive estimate of the intra-class correlation while the “geese” function gives a negative estimate. Neither of the confidence intervals contains the value zero. Conclusions The “gls” function efficiently produces an estimate of the intra-class correlation with a confidence interval. When the within-group correlation is as high as 0.5, the confidence interval is not always obtainable. Supplementary Information The online version contains supplementary material available at (10.1186/s13063-021-05900-7).
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Affiliation(s)
- Wei Wang
- Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Michael O Harhay
- Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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42
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Balzer LB, van der Laan M, Ayieko J, Kamya M, Chamie G, Schwab J, Havlir DV, Petersen ML. Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials. Biostatistics 2021; 24:502-517. [PMID: 34939083 PMCID: PMC10102904 DOI: 10.1093/biostatistics/kxab043] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/19/2021] [Accepted: 11/15/2021] [Indexed: 11/14/2022] Open
Abstract
Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we propose and evaluate a novel two-stage targeted minimum loss-based estimator to adjust for baseline covariates in a manner that optimizes precision, after controlling for baseline and postbaseline causes of missing outcomes. Finite sample simulations illustrate that our approach can nearly eliminate bias due to differential outcome measurement, while existing CRT estimators yield misleading results and inferences. Application to real data from the SEARCH community randomized trial demonstrates the gains in efficiency afforded through adaptive adjustment for baseline covariates, after controlling for missingness on individual-level outcomes.
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Affiliation(s)
- Laura B Balzer
- Department of Biostatistics & Epidemiology, University of Massachusetts Amherst, 715 North Pleasant St, Amherst, MA, USA
| | - Mark van der Laan
- Division of Biostatistics, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA, USA
| | - James Ayieko
- Center for Microbiology Research, Kenya Medical Research Institute, P.O. BOX 54840 00200 Off Raila Odinga Way, Nairobi, Kenya
| | - Moses Kamya
- Department of Medicine, Makerere University and the Infectious Diseases Research Collaboration, P.O Box 7475, Kampala, Uganda
| | - Gabriel Chamie
- Department of Medicine, University of California San Francisco, 995 Potrero Ave, San Francisco, CA, USA
| | - Joshua Schwab
- Division of Biostatistics, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA, USA
| | - Diane V Havlir
- Department of Medicine, University of California San Francisco, 995 Potrero Ave, San Francisco, CA, USA
| | - Maya L Petersen
- Division of Biostatistics, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA, USA
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McKay MH, Pickens ND, Medley A, Evetts CL. Outcomes of Team-Centered, Occupational Adaptation-Based Versus Traditional Dementia Workforce Training : Résultats comparés d'une formation axée sur l'équipe et l'adaptation occupationnelle et de l'approche de formation traditionnelle pour le personnel travaillant auprès des personnes atteintes de démence. The Canadian Journal of Occupational Therapy 2021; 88:384-394. [PMID: 34668409 DOI: 10.1177/00084174211048017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background. Dementia workforce training aligned with Occupational Adaptation (OA) theory may facilitate teams resolving challenges in the care environment more than traditional skills-based (SB) training, although comparisons are needed. Purpose. This pilot study compared effectiveness of an OA and SB program on relative mastery and team development for dementia care teams at a continuing care retirement community. Method. In a quasi-experimental study, employees underwent nine sessions in an OA or SB program. Relative Mastery Measurement Scale and Team Development Measure results were collected pre-, mid-, post-intervention. A 3 × 2 ANOVA determined differences in group score changes across time. Findings. Data from 28 employees (14/group) showed group-by-time interaction reached statistical significance for both relative mastery (F = 3.17, df = 2, p = .05) and team development (F = 8.38, df = 2, p = .001). Implications. OA-based training may improve dementia care teams' collaborative mastery over real-world challenges. While preliminary findings inform program developers, further research must explore clinical effectiveness.
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44
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Easter C, Thompson JA, Eldridge S, Taljaard M, Hemming K. Cluster randomized trials of individual-level interventions were at high risk of bias. J Clin Epidemiol 2021; 138:49-59. [PMID: 34197941 PMCID: PMC8592576 DOI: 10.1016/j.jclinepi.2021.06.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/12/2021] [Accepted: 06/22/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVES To describe the prevalence of risks of bias in cluster-randomized trials of individual-level interventions, according to the Cochrane Risk of Bias tool. STUDY DESIGN AND SETTING Review undertaken in duplicate of a random sample of 40 primary reports of cluster-randomized trials of individual-level interventions. RESULTS The most common reported reasons for adopting cluster randomization were the need to avoid contamination (17, 42.5%) and practical considerations (14, 35%). Of the 40 trials all but one was assessed as being at risk of bias. A majority (27, 67.5%) were assessed as at risk due to the timing of identification and recruitment of participants; many (21, 52.5%) due to an apparent lack of adequate allocation concealment; and many due to selectively reported results (22, 55%), arising from a mixture of reasons including lack of documentation of primary outcome. Other risks mostly occurred infrequently. CONCLUSION Many cluster-randomized trials evaluating individual-level interventions appear to be at risk of bias, mostly due to identification and recruitment biases. We recommend that investigators carefully consider the need for cluster randomization; follow recommended procedures to mitigate risks of identification and recruitment bias; and adhere to good reporting practices including clear documentation of primary outcome and allocation concealment methods.
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Affiliation(s)
- Christina Easter
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jennifer A Thompson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Sandra Eldridge
- Centre for Clinical Trials and Methodology, Queen Mary University of London, London
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
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45
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Using cluster-robust standard errors when analyzing group-randomized trials with few clusters. Behav Res Methods 2021; 54:1181-1199. [PMID: 34505994 DOI: 10.3758/s13428-021-01627-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2021] [Indexed: 11/08/2022]
Abstract
Accounting for dependent observations in cluster-randomized trials (CRTs) using nested data is necessary in order to avoid misestimated standard errors resulting in questionable inferential statistics. Cluster-robust standard errors (CRSEs) are often used to address this issue. However, CRSEs are still well-known to underestimate standard errors for group-level variables when the number of clusters is low (e.g., < 50) and with CRTs, a small number of clusters, due to logistical or financial considerations, is the norm rather than the exception. Using a simulation with various conditions, we investigate the use of a small sample correction (i.e., CR2 estimator) proposed by Bell and McCaffrey (2002) together with empirically derived degrees of freedom estimates (dofBM). Findings indicate that even with as few as 10 clusters, the CR2 estimator used with dofBM yields generally unbiased results with acceptable type I error and coverage rates. Results show that coverage and type I error rates can be largely influenced by the choice of dof, not just the standard error adjustments. An applied example is provided together with R syntax to conduct the analysis. To facilitate the use of different CRSEs, a free graphical, menu-driven SPSS add-on to compute the various cluster-robust variance estimates can be downloaded from https://github.com/flh3/CR2/tree/master/SPSS .
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46
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Su F, Ding P. Model‐assisted analyses of cluster‐randomized experiments. J R Stat Soc Series B Stat Methodol 2021. [DOI: 10.1111/rssb.12468] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Fangzhou Su
- Department of Statistics University of California Berkeley California USA
| | - Peng Ding
- Department of Statistics University of California Berkeley California USA
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47
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Gurka MJ. Cluster Randomized Trials in Pediatric Research. J Pediatr 2021; 236:333-334. [PMID: 34126121 DOI: 10.1016/j.jpeds.2021.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 11/24/2022]
Affiliation(s)
- Matthew J Gurka
- Department of Health Outcomes and Biomedical Informatics, Institute for Child Health Policy, University of Florida, Gainesville, FL.
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48
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Profit J, Adair KC, Cui X, Mitchell B, Brandon D, Tawfik DS, Rigdon J, Gould JB, Lee HC, Timpson WL, McCaffrey MJ, Davis AS, Pammi M, Matthews M, Stark AR, Papile LA, Thomas E, Cotten M, Khan A, Sexton JB. Randomized controlled trial of the "WISER" intervention to reduce healthcare worker burnout. J Perinatol 2021; 41:2225-2234. [PMID: 34366432 PMCID: PMC8440181 DOI: 10.1038/s41372-021-01100-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/26/2021] [Accepted: 05/06/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Test web-based implementation for the science of enhancing resilience (WISER) intervention efficacy in reducing healthcare worker (HCW) burnout. DESIGN RCT using two cohorts of HCWs of four NICUs each, to improve HCW well-being (primary outcome: burnout). Cohort 1 received WISER while Cohort 2 acted as a waitlist control. RESULTS Cohorts were similar, mostly female (83%) and nurses (62%). In Cohorts 1 and 2 respectively, 182 and 299 initiated WISER, 100 and 176 completed 1-month follow-up, and 78 and 146 completed 6-month follow-up. Relative to control, WISER decreased burnout (-5.27 (95% CI: -10.44, -0.10), p = 0.046). Combined adjusted cohort results at 1-month showed that the percentage of HCWs reporting concerning outcomes was significantly decreased for burnout (-6.3% (95%CI: -11.6%, -1.0%); p = 0.008), and secondary outcomes depression (-5.2% (95%CI: -10.8, -0.4); p = 0.022) and work-life integration (-11.8% (95%CI: -17.9, -6.1); p < 0.001). Improvements endured at 6 months. CONCLUSION WISER appears to durably improve HCW well-being. CLINICAL TRIALS NUMBER NCT02603133; https://clinicaltrials.gov/ct2/show/NCT02603133.
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Affiliation(s)
- Jochen Profit
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA
- California Perinatal Quality Care Collaborative, Palo Alto, CA, USA
| | - Kathryn C Adair
- Department of Psychiatry, Duke University School of Medicine, Duke University Health System, Durham, NC, USA
- Duke Center for Healthcare Safety and Quality, Duke University Health System, Durham, NC, USA
| | - Xin Cui
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA
- California Perinatal Quality Care Collaborative, Palo Alto, CA, USA
| | - Briana Mitchell
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA
| | - Debra Brandon
- Duke University School of Nursing, Durham, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, USA
| | - Daniel S Tawfik
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA
| | - Joseph Rigdon
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jeffrey B Gould
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA
- California Perinatal Quality Care Collaborative, Palo Alto, CA, USA
| | - Henry C Lee
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA
- California Perinatal Quality Care Collaborative, Palo Alto, CA, USA
| | - Wendy L Timpson
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Martin J McCaffrey
- Division of Neonatal-Perinatal Medicine, University of North Carolina Chapel Hill School of Medicine and University of North Carolina Children's Hospital, Chapel Hill, NC, USA
| | - Alexis S Davis
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA
| | - Mohan Pammi
- Section of Neonatology, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA
| | - Melissa Matthews
- Department of Pediatrics-Neonatology, The University of Texas Health Science Center and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Ann R Stark
- Department of Pediatrics, Division of Newborn Medicine, Harvard Medical School, Boston, MA, USA
| | - Lu-Ann Papile
- Division of Neonatology, Department of Pediatrics, The University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Eric Thomas
- Department of Internal Medicine, The University of Texas Health Science Center and Memorial Hermann Medical Center, Houston, TX, USA
| | - Michael Cotten
- Division of Pediatrics-Neonatology, Duke University School of Medicine and Duke University Hospital, Durham, NC, USA
| | - Amir Khan
- Division of Neonatology, Department of Pediatrics, The University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - J Bryan Sexton
- Department of Psychiatry, Duke University School of Medicine, Duke University Health System, Durham, NC, USA.
- Duke Center for Healthcare Safety and Quality, Duke University Health System, Durham, NC, USA.
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Zhan D, Xu L, Ouyang Y, Sawatzky R, Wong H. Methods for dealing with unequal cluster sizes in cluster randomized trials: A scoping review. PLoS One 2021; 16:e0255389. [PMID: 34324593 PMCID: PMC8320970 DOI: 10.1371/journal.pone.0255389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 07/15/2021] [Indexed: 11/18/2022] Open
Abstract
In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes-the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.
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Affiliation(s)
- Denghuang Zhan
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Health Evaluation and Outcomes Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Liang Xu
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Health Evaluation and Outcomes Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Yongdong Ouyang
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Health Evaluation and Outcomes Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Richard Sawatzky
- Centre for Health Evaluation and Outcomes Sciences, University of British Columbia, Vancouver, British Columbia, Canada
- School of Nursing, Trinity Western University, Langley City, British Columbia, Canada
| | - Hubert Wong
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Health Evaluation and Outcomes Sciences, University of British Columbia, Vancouver, British Columbia, Canada
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50
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Andersen E, Geiger P, Schiller C, Bluth K, Watkins L, Zhang Y, Xia K, Tauseef H, Leserman J, Gaylord S, Girdler S. Effects of Mindfulness-Based Stress Reduction on Experimental Pain Sensitivity and Cortisol Responses in Women With Early Life Abuse: A Randomized Controlled Trial. Psychosom Med 2021; 83:515-527. [PMID: 33259351 PMCID: PMC8164640 DOI: 10.1097/psy.0000000000000889] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Early life abuse (ELAb) initiates pathophysiological cascades resulting in long-term maladaptive stress responsivity, hyperalgesia, and an increased risk of psychopathology. Mindfulness-based stress reduction (MBSR) is effective in modifying psychological and somatic symptoms; thus, we predicted that MBSR would be particularly efficacious for women with ELAb. METHOD Medically healthy women (mean age = 31 years) with or without a history of early (≤13 years) physical or sexual abuse provided self-report measures and were tested in the laboratory before and after randomization to standard MBSR (n = 52) or social support (SSG) (n = 60) for 8 weeks. The laboratory procedure involved pain testing using the cold pressor and temporal summation of heat pain (indexing central sensitization) procedures, and exposure to the Trier Social Stress Test. Plasma cortisol in response to the experimental protocol was assessed as area under the curve (AUC). RESULTS The interventions differentially impacted pain sensitivity and cortisol AUC for women with ELAb, as MBSR increased the temporal summation of heat pain intensity ratings (p = .024) and reduced cortisol AUC (p = .004). For women without ELAb, MBSR decreased cold pressor tolerance (p = .045) and decreased the temporal summation of heat pain intensity ratings relative to SSG (p = .024). Both MBSR and SSG improved depression symptoms and emotion regulation abilities (p values < .001); however, MBSR was associated with greater benefits in describing emotions (p = .008) and impulse control (p = .017) for women with ELAb. CONCLUSIONS Women with ELAb benefited from MBSR-specific improvements in central sensitization, mindfulness skills, and emotion regulation abilities. This is the first study to examine the efficacy of MBSR in modifying affective and somatic symptoms based on ELAb status and provides evidence for considering ELAb in tailoring treatment approaches.Trial Registration: ClinicalTrials.gov Identifier: NCT01995916; https://clinicaltrials.gov/ct2/show/NCT01995916.
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Affiliation(s)
| | - Paul Geiger
- Department of Psychiatry, University of North Carolina-Chapel Hill
| | - Crystal Schiller
- Department of Psychiatry, University of North Carolina-Chapel Hill
| | - Karen Bluth
- Department of Psychiatry, University of North Carolina-Chapel Hill
| | - Lana Watkins
- Department of Psychiatry and Behavioral Sciences, Duke
University
| | - Ying Zhang
- Department of Biostatistics, University of North Carolina- Chapel
Hill
| | - Kai Xia
- Department of Psychiatry, University of North Carolina-Chapel Hill
| | - Hafsah Tauseef
- Department of Psychiatry, University of North Carolina-Chapel Hill
| | - Jane Leserman
- Department of Psychiatry, University of North Carolina-Chapel Hill
| | - Susan Gaylord
- Department of Physical Medicine and Rehabilitation, University of
North Carolina-Chapel Hill
| | - Susan Girdler
- Department of Psychiatry, University of North Carolina-Chapel
Hill
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