Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity

Tong, Guangyu; Esserman, Denise; Li, Fan

doi:10.1002/sim.9283

Cited by 17 publications

(45 citation statements)

References 36 publications

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“…urban vs rural, differences in patient characteristics, staffing capacity or expertise in each cluster, etc.). Thus, it may be more relevant from a policy-making perspective to explore variation in treatment effects by these types of factors 39 , 40 rather than by cluster size.…”

Section: Examplementioning

confidence: 99%

Estimands in cluster-randomized trials: choosing analyses that answer the right question

Kahan

Copas

et al. 2022

International Journal of Epidemiology

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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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Section: Examplementioning

confidence: 99%

Estimands in cluster-randomized trials: choosing analyses that answer the right question

Kahan

Copas

et al. 2022

International Journal of Epidemiology

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“… 37 Methodology for detecting treatment‐effect heterogeneity in cluster randomized trials is the topic of ongoing research. 38 , 39 …”

Section: Discussionmentioning

confidence: 99%

“…However, recent methodological results have revealed that detecting treatment‐effect heterogeneity in cluster randomized trials may not always require larger sample sizes than detecting the average treatment effect 37 . Methodology for detecting treatment‐effect heterogeneity in cluster randomized trials is the topic of ongoing research 38,39 …”

Section: Discussionmentioning

confidence: 99%

“…37 Methodology for detecting treatment-effect heterogeneity in cluster randomized trials is the topic of ongoing research. 38,39 Finally, while equity considerations are context dependent, it is insufficient to only consider age and sex. Given the evidence of outcome-related inequities by race/ethnicity, education, socioeconomic status, and social capital, equity-relevant trials should carefully consider PROGRESS-Plus factors in their objectives, design, analysis and reporting, particularly in circumstances where there is disproportionate need or unequal impacts on equity-relevant groups.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

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Health equity considerations in pragmatic trials in Alzheimer's and dementia disease: Results from a methodological review

Nicholls

Al‐Jaishi

Niznick

et al. 2023

Alz & Dem Diag Ass & Dis Mo

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Introduction To improve dementia care delivery for persons across all backgrounds, it is imperative that health equity is integrated into pragmatic trials. Methods We reviewed 62 pragmatic trials of people with dementia published 2014 to 2019. We assessed health equity in the objectives; design, conduct, analysis; and reporting using PROGRESS‐Plus which stands for Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, and other factors such as age and disability. Results Two (3.2%) trials incorporated equity considerations into their objectives; nine (14.5%) engaged with communities; 4 (6.5%) described steps to increase enrollment from equity‐relevant groups. Almost all trials (59, 95.2%) assessed baseline balance for at least one PROGRESS‐Plus characteristic, but only 10 (16.1%) presented subgroup analyses across such characteristics. Differential recruitment, attrition, implementation, adherence, and applicability across PROGRESS‐Plus were seldom discussed. Discussion Ongoing and future pragmatic trials should more rigorously integrate equity considerations in their design, conduct, and reporting. Highlights Few pragmatic trials are explicitly designed to inform equity‐relevant objectives. Few pragmatic trials take steps to increase enrollment from equity‐relevant groups. Disaggregated results across equity‐relevant groups are seldom reported. Adherence to existing tools (e.g., IMPACT Best Practices, CONSORT‐Equity) is key.

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“…Although the power analysis of the treatment-by-covariate interaction test has been relatively well-studied in individually randomized trials, 3,4,5 related methods for power analysis in CRTs have only received recent attention with the goal to enable a rigorous understanding of how system-level innovations may differentially impact outcomes for important subpopulations. 6,7,8,9,10 With a pre-specified effect modifier, Yang et al 8 developed an analytical sample size and power formula to test the treatmentby-covariate interaction, making it possible to power CRTs a priori for confirmatory HTE analyses. Similar to designing conventional CRTs to study the average treatment effect, the intracluster correlation coefficient (ICC) of the outcome, or outcome-ICC, plays an essential role in determining the power and necessary sample size for the HTE test.…”

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confidence: 99%

Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity

Ryan¹,

Esserman²,

Li³

2023

Preprint

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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 prespecified 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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Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity

Cited by 17 publications

References 36 publications

Estimands in cluster-randomized trials: choosing analyses that answer the right question

Estimands in cluster-randomized trials: choosing analyses that answer the right question

Health equity considerations in pragmatic trials in Alzheimer's and dementia disease: Results from a methodological review

Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity

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