Generalizability and transportability in clinical trials

临床试验的主要目的之一是估计一种治疗在一般人群中的潜在因果效应。临床试验的“黄金标准”是随机对照试验（RCT），通过比较对照组和治疗组之间的结果来评估因果关系。然而，RCT参与者的样本通常不能代表接受治疗的人群。虽然估计的因果效应对随机对照试验样本来说是内部无偏的，但对目标人群来说可能不是外部有效的。另一方面，观察性研究通常更能代表目标人群，但由于混淆，可能会受到其他内部偏差的影响。这个博士项目的目的是评估和提高临床试验治疗效果估计的普遍性和可移植性。为此，学生将发展严格和有原则的统计方法，结合随机对照试验和观察性研究的推论。基于登记的随机对照试验，如ASCEND试验，特别有望改善推广性，因为参与者直接从健康和疾病登记中招募，因此更能代表目标人群。学生将通过广泛的模拟和实际临床试验数据的应用来评估开发的方法。

One of the principal aims of a clinical trial is to estimate the underlying causal effect of a treatment in the general population. The 'gold-standard' for a clinical trial is the randomized controlled trial (RCT), whereby causal effects are assessed by comparing outcomes between the control and the treated group. Often, however, the sample of RCT participants is not representative of the population in which the treatment is delivered. While the estimated causal effect will be internally unbiased for the RCT sample, it may not be externally valid for the target population. Observational studies, on the other hand, are typically more representative of the target population but can be subject to other internal biases due to confounding.The aim of this PhD project is to assess and improve the generalizability and transportability of treatment effect estimates from clinical trials. To that end, the student will develop rigorous and principled statistical methodology to combine inferences from RCTs and observational studies. Registry-based RCTs, such as the ASCEND trial, offer particular promise for improved generalizability as participants are recruited directly from health and disease registries and are thus more representative of the target population. The student will assess the developed methodology through both extensive simulations and application to real clinical trial data.