TY - JOUR

T1 - Unbiased and efficient estimation of causal treatment effects in crossover trials

AU - Madsen, Jeppe Ekstrand Halkjær

AU - Scheike, Thomas

AU - Pipper, Christian

N1 - Publisher Copyright: © 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - bias

KW - causal inference

KW - crossover trials

KW - efficiency

KW - TQT studies

U2 - 10.1002/bimj.202200170

DO - 10.1002/bimj.202200170

M3 - Journal article

C2 - 37551995

AN - SCOPUS:85166960149

VL - 65

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 8

M1 - 2200170

ER -