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Staggered treatment adoption arises in the evaluation of policy impact and implementation in a variety of settings. This occurs in both randomized stepped-wedge trials and non-randomized quasi-experimental designs using causal inference methods based on difference-in-differences analysis. In both settings, it is crucial to carefully consider the target estimand and possible treatment effect heterogeneities in order to estimate the effect without bias and in an interpretable fashion. This paper proposes a novel non-parametric approach to this estimation for either setting. By constructing an estimator using two-by-two difference-in-difference comparisons as building blocks with arbitrary weights, the investigator can select weights to target the desired estimand in an unbiased manner under assumed treatment effect homogeneity, and minimize the variance under an assumed working covariance structure. This provides desirable bias properties with a relatively small sacrifice in variance and power by using the comparisons efficiently. The method is demonstrated on toy examples to show the process, as well as in the re-analysis of a stepped wedge trial on the impact of novel tuberculosis diagnostic tools. A full algorithm with R code is provided to implement this method. The proposed method allows for high flexibility and clear targeting of desired effects, providing one solution to the bias-variance-generalizability tradeoff.
Lee Kennedy‐Shaffer (Tue,) studied this question.