Based on a permutation argument, we derive a closed form expression for an estimate of the treatment effect, along with its standard error, in a stepped wedge design. We show that these estimates are robust to misspecification of both the mean and covariance structure of the underlying data-generating mechanism, thereby providing a robust approach to inference for the treatment effect in stepped wedge designs. We use simulations to evaluate the type I error and power of the proposed estimate and to compare the performance of the proposed estimate to the optimal estimate when the correct model specification is known. The limitations, possible extensions, and open problems regarding the method are discussed.
Biostatistics | Clinical Trials | Statistical Methodology
Hughes, James P.; Heagerty, Patrick J.; Xia, Fan; and Ren, Yuqi, "Robust Inference for the Stepped Wedge Design" (August 2018). UW Biostatistics Working Paper Series. Working Paper 424.