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We introduce causal inference reasoning to cross-over trials, with a focus onThorough QT (TQT) studies. For such trials, we propose different sets ofassumptions and consider their impact on the modelling strategy and estimationprocedure. We show that unbiased estimates of a causal treatment effect areobtained by a G-computation approach in combination with weighted least squarespredictions from a working regression model. Only a few natural requirements onthe 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 tounbiased estimates of a causal treatment effect, even if they do not capturethe true data generating mechanism. We compare a range of working regressionmodels in a simulation study where data are simulated from a complex datagenerating mechanism with input parameters estimated on a real TQT data set. Inthis setting, we find that for all practical purposes working models adjustingfor baseline QTc measurements have comparable performance. Specifically, thisis observed for working models that are by default too simplistic to capturethe true data generating mechanism. Cross-over trials and particularly TQTstudies can be analysed efficiently using simple working regression modelswithout biasing the estimates for the causal parameters of interest.

Unbiased and Efficient Estimation of Causal Treatment Effects in Cross-over Trials