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Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect. Statistics in Medicine. 2022; 1– 29. doi:10.1002/sim.9511, , , , .
Stepped wedge cluster randomized controlled trials are often used to evaluated effectiveness during the phased rollout of an intervention. Stepped wedge trials are typically analyzed using models that assume the full effect of the treatment is achieved instantaneously. We provide an analytical framework for scenarios in which the treatment effect varies as a function of exposure time (time since the start of treatment) and define the “effect curve” as the magnitude of the treatment effect as a function of exposure time. The “time-averaged treatment effect” (TATE) and “long-term treatment effect” (LTE) are two possible summaries of this curve. We show that if the true treatment effect varies with exposure time, but the data are analyzed with a model that assumes an immediate treatment effect, then estimates can be severely misleading. In some cases, the treatment effect estimator will converge to a value of the opposite sign of the true TATE or LTE. To avoid this problem, we describe several models, some of which make assumptions about the shape of the effect curve, that can be used to simultaneously estimate the entire effect curve, the TATE, and/or the LTE. We evaluate these models in a simulation study and apply them to two real datasets. We recommend that a model that accounts for time-varying treatment effects is always used in stepped wedge trials moving forward unless the researcher has compelling evidence that the treatment effect is immediate and sustained. Furthermore, it may be worth reanalyzing data from past stepped wedge trials in which the immediate treatment effect assumption may not hold.