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Nevins P, Ryan M, Davis-Plourde K, Ouyang Y, Macedo JAP, Meng C, Tong G, Wang X, Ortiz-Reyes L, Caille A, Li F, Taljaard M. Adherence to key recommendations for design and analysis of stepped-wedge cluster randomized trials: A review of trials published 2016-2022. Clin Trials 2024; 21:199-210. [PMID: 37990575 PMCID: PMC11003836 DOI: 10.1177/17407745231208397] [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: 11/23/2023]
Abstract
BACKGROUND/AIMS The stepped-wedge cluster randomized trial (SW-CRT), in which clusters are randomized to a time at which they will transition to the intervention condition - rather than a trial arm - is a relatively new design. SW-CRTs have additional design and analytical considerations compared to conventional parallel arm trials. To inform future methodological development, including guidance for trialists and the selection of parameters for statistical simulation studies, we conducted a review of recently published SW-CRTs. Specific objectives were to describe (1) the types of designs used in practice, (2) adherence to key requirements for statistical analysis, and (3) practices around covariate adjustment. We also examined changes in adherence over time and by journal impact factor. METHODS We used electronic searches to identify primary reports of SW-CRTs published 2016-2022. Two reviewers extracted information from each trial report and its protocol, if available, and resolved disagreements through discussion. RESULTS We identified 160 eligible trials, randomizing a median (Q1-Q3) of 11 (8-18) clusters to 5 (4-7) sequences. The majority (122, 76%) were cross-sectional (almost all with continuous recruitment), 23 (14%) were closed cohorts and 15 (9%) open cohorts. Many trials had complex design features such as multiple or multivariate primary outcomes (50, 31%) or time-dependent repeated measures (27, 22%). The most common type of primary outcome was binary (51%); continuous outcomes were less common (26%). The most frequently used method of analysis was a generalized linear mixed model (112, 70%); generalized estimating equations were used less frequently (12, 8%). Among 142 trials with fewer than 40 clusters, only 9 (6%) reported using methods appropriate for a small number of clusters. Statistical analyses clearly adjusted for time effects in 119 (74%), for within-cluster correlations in 132 (83%), and for distinct between-period correlations in 13 (8%). Covariates were included in the primary analysis of the primary outcome in 82 (51%) and were most often individual-level covariates; however, clear and complete pre-specification of covariates was uncommon. Adherence to some key methodological requirements (adjusting for time effects, accounting for within-period correlation) was higher among trials published in higher versus lower impact factor journals. Substantial improvements over time were not observed although a slight improvement was observed in the proportion accounting for a distinct between-period correlation. CONCLUSIONS Future methods development should prioritize methods for SW-CRTs with binary or time-to-event outcomes, small numbers of clusters, continuous recruitment designs, multivariate outcomes, or time-dependent repeated measures. Trialists, journal editors, and peer reviewers should be aware that SW-CRTs have additional methodological requirements over parallel arm designs including the need to account for period effects as well as complex intracluster correlations.
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Affiliation(s)
- Pascale Nevins
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Mary Ryan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Kendra Davis-Plourde
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Yongdong Ouyang
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Can Meng
- Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Guangyu Tong
- 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
| | - 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
| | - Luis Ortiz-Reyes
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Agnès Caille
- Université de Tours, Université de Nantes, INSERM, SPHERE U1246, Tours, France
- INSERM CIC 1415, CHRU de Tours, Tours, France
| | - 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
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
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Patole S, Pawale D, Rath C. Interventions for Compassion Fatigue in Healthcare Providers-A Systematic Review of Randomised Controlled Trials. Healthcare (Basel) 2024; 12:171. [PMID: 38255060 PMCID: PMC10815881 DOI: 10.3390/healthcare12020171] [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: 11/29/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Compassion fatigue is a significant issue considering its consequences including negative feelings, impaired cognition, and increased risk of long-term morbidities. We aimed to assess current evidence on the effects of interventions for compassion fatigue in healthcare providers (HCP). METHODS We used the Cochrane methodology for Systematic Reviews and Meta-Analyses (PRISMA) for conducting and reporting this review. RESULTS Fifteen RCTs (n = 1740) were included. The sample size of individual studies was small ranging from 23 to 605. There was significant heterogeneity in participant, intervention, control, and outcome characteristics. The tools for assessing intervention effects on compassion fatigue included ProQOL, compassion fatigue scale, and nurses compassion fatigue inventory. Thirteen out of the fifteen included RCTs had overall high risk of bias (ROB). Meta-analysis could not be performed given the significant heterogeneity. CONCLUSIONS Current evidence on interventions for reducing compassion fatigue in HCPs is inadequate. Given the benefits reported in some of the included studies, well-designed and adequately powered RCTs are urgently needed.
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Affiliation(s)
- Sanjay Patole
- Neonatal Directorate, KEM Hospital for Women, Perth, WA 6008, Australia; (D.P.); (C.R.)
- School of Medicine, University of Western Australia, Perth, WA 6009, Australia
| | - Dinesh Pawale
- Neonatal Directorate, KEM Hospital for Women, Perth, WA 6008, Australia; (D.P.); (C.R.)
| | - Chandra Rath
- Neonatal Directorate, KEM Hospital for Women, Perth, WA 6008, Australia; (D.P.); (C.R.)
- School of Medicine, University of Western Australia, Perth, WA 6009, Australia
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Singh SP. Bayesian optimal stepped wedge design. Biom J 2024; 66:e2300168. [PMID: 38057145 DOI: 10.1002/bimj.202300168] [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: 06/20/2023] [Revised: 09/17/2023] [Accepted: 10/13/2023] [Indexed: 12/08/2023]
Abstract
Recently, there has been a growing interest in designing cluster trials using stepped wedge design (SWD). An SWD is a type of cluster-crossover design in which clusters of individuals are randomized unidirectional from a control to an intervention at certain time points. The intraclass correlation coefficient (ICC) that measures the dependency of subject within a cluster plays an important role in design and analysis of stepped wedge trials. In this paper, we discuss a Bayesian approach to address the dependency of SWD on the ICC and robust Bayesian SWDs are proposed. Bayesian design is shown to be more robust against the misspecification of the parameter values compared to the locally optimal design. Designs are obtained for the various choices of priors assigned to the ICC. A detailed sensitivity analysis is performed to assess the robustness of proposed optimal designs. The power superiority of Bayesian design against the commonly used balanced design is demonstrated numerically using hypothetical as well as real scenarios.
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Affiliation(s)
- Satya Prakash Singh
- Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
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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 2023:10.1007/s11121-023-01590-6. [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] [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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