We introduce causal inference reasoning to cross-over trials, with a focus on Thorough QT (TQT) studies. For such trials, we propose different sets of assumptions and consider their impact on the modelling 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. Cross-over trials and particularly TQT studies can be analysed efficiently using simple working regression models without biasing the estimates for the causal parameters of interest.
翻译:我们为交叉审判引入了因果推断推理,重点是Thorough QT(TQT)研究。关于此类试验,我们提出不同的假设,并考虑其对建模战略和估计程序的影响。我们表明,G计算法与工作回归模型中加权最小正方数预测相结合,对因果关系处理效果的不偏倚估计是获得的。只有工作回归和加权矩阵的少数自然要求才能维持结果。因此,一大批高萨线性线性混合工作模型导致对因果处理效果的不偏倚估计,即使它们不捕捉真正的数据生成机制。我们在模拟研究中比较一系列工作回归模型,从复杂的数据生成机制中模拟数据,同时对真实的TQT数据集进行输入参数估计。在这种背景下,我们发现,为了所有实际目的,调整QTC基准测量的工作模型都具有可比较性。具体地说,对于由于违约过于简单化而无法捕捉到真实数据生成机制的工作模型来说,这是观察到的。交叉试验,特别是TQT研究可以对一系列工作回归模型进行高效的分析,而不用简单的因果关系分析。