Unbiased and efficient estimation of causal treatment effects in crossover trials

We introduce causal inference reasoning to crossover trials, with a focus on thorough QT (TQT) studies. For such trials, we propose different sets of assumptions and consider their impact on the modeling strategy and estimation procedure. We show that unbiased estimates of a causal treatment effect are obtained by a g-computation approach in combination with weighted least squares predictions from a working regression model. Only a few natural requirements on the working regression and weighting matrix are needed for the result to hold. It follows that a large class of Gaussian linear mixed working models lead to unbiased estimates of a causal treatment effect, even if they do not capture the true data-generating mechanism. We compare a range of working regression models in a simulation study where data are simulated from a complex data-generating mechanism with input parameters estimated on a real TQT data set. In this setting, we find that for all practical purposes working models adjusting for baseline QTc measurements have comparable performance. Specifically, this is observed for working models that are by default too simplistic to capture the true data-generating mechanism. Crossover trials and particularly TQT studies can be analyzed efficiently using simple working regression models without biasing the estimates for the causal parameters of interest.

Automated summary

This article introduces causal inference reasoning into crossover trials, focusing on thorough QT (TQT) studies. Different sets of assumptions and their impact on modeling strategy and estimation procedure are proposed for such trials. Using a g-computation approach and weighted least squares predictions from a working regression model, unbiased estimates of a causal treatment effect can be obtained. The article shows that a large class of Gaussian linear mixed working models can lead to unbiased estimates of a causal treatment effect, even if they do not capture the true data-generating mechanism. In a simulation study comparing various working regression models, it is found that working models adjusting for baseline QTc measurements have comparable performance, even if they are too simplistic to capture the true data-generating mechanism. Crossover trials and TQT studies can be effectively analyzed using simple working regression models without biasing the estimates for the causal parameters of interest.