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Randomization‐based inference for a marginal treatment effect in stepped wedge cluster randomized trials
Author(s) -
Rabideau Dustin J.,
Wang Rui
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9040
Subject(s) - randomization , inference , marginal structural model , randomized controlled trial , wedge (geometry) , statistics , marginal model , cluster randomised controlled trial , econometrics , cluster (spacecraft) , causal inference , mathematics , computer science , medicine , artificial intelligence , geometry , regression analysis , programming language
In a cross‐sectional stepped wedge cluster randomized trial (SWT), clusters are randomized to crossover from control to intervention at different time periods and outcomes are assessed for a different set of individuals in each cluster‐period. Randomization‐based inference is an attractive analysis strategy for SWTs because it does not require full parametric specification of the outcome distribution or correlation structure and its validity does not rely on having a large number of clusters. Existing randomization‐based approaches for SWTs, however, either focus on hypothesis testing and omit technical details on confidence interval (CI) calculation with noncontinuous outcomes, or employ weighted cluster‐period summary statistics for p ‐value and CI calculation, which can result in suboptimal efficiency if weights do not incorporate information on varying cluster‐period sizes. In this article, we propose a framework for calculating randomization‐based p ‐values and CIs for a marginal treatment effect in SWTs by using test statistics derived from individual‐level generalized linear models. We also investigate how study design features, such as stratified randomization, subsequently impact various SWT analysis methods including the proposed approach. Data from the XpertMTB/RIF tuberculosis trial are reanalyzed to illustrate our method and compare it to alternatives.