Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect

Stepped wedge cluster randomized controlled 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 on the linear predictor scale as a function of exposure time. The time-averaged treatment effect (TATE) and long-term treatment effect (LTE) are summaries of this curve. We analytically derive the expectation of the estimator delta<^>$$ \hat{\delta} $$ resulting from a model that assumes an immediate treatment effect and show that it can be expressed as a weighted sum of the time-specific treatment effects corresponding to the observed exposure times. Surprisingly, although the weights sum to one, some of the weights can be negative. This implies that delta<^>$$ \hat{\delta} $$ may be severely misleading and can even converge to a value of the opposite sign of the true TATE or LTE. 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 the LTE. We evaluate these models in a simulation study to examine the operating characteristics of the resulting estimators and apply them to two real datasets.

Automated summary

This article presents an analytical framework for analyzing stepped wedge cluster randomized controlled trials that considers the variation of the treatment effect over exposure time. The authors derive the expectation of the estimator under the assumption of immediate treatment effect and show that it can be misleading. They also introduce several models that can simultaneously estimate the entire effect curve, time-averaged treatment effect, and long-term treatment effect.