I-BioStat, KU Leuven, Leuven, Belgium.
I-BioStat, Universiteit Hasselt, Hasselt, Belgium.
Stat Methods Med Res. 2024 Jul;33(7):1278-1296. doi: 10.1177/09622802241259177. Epub 2024 Jul 25.
The selection of the primary endpoint in a clinical trial plays a critical role in determining the trial's success. Ideally, the primary endpoint is the clinically most relevant outcome, also termed the true endpoint. However, practical considerations, like extended follow-up, may complicate this choice, prompting the proposal to replace the true endpoint with so-called surrogate endpoints. Evaluating the validity of these surrogate endpoints is crucial, and a popular evaluation framework is based on the proportion of treatment effect explained (PTE). While methodological advancements in this area have focused primarily on estimation methods, interpretation remains a challenge hindering the practical use of the PTE. We review various ways to interpret the PTE. These interpretations-two causal and one non-causal-reveal connections between the PTE principal surrogacy, causal mediation analysis, and the prediction of trial-level treatment effects. A common limitation across these interpretations is the reliance on unverifiable assumptions. As such, we argue that the PTE is only meaningful when researchers are willing to make very strong assumptions. These challenges are also illustrated in an analysis of three hypothetical vaccine trials.
临床试验中主要终点的选择对于确定试验的成功至关重要。理想情况下，主要终点是最具临床相关性的结果，也称为真实终点。然而，实际考虑因素，如延长随访时间，可能会使这种选择变得复杂，促使人们提出用所谓的替代终点来替代真实终点。评估这些替代终点的有效性至关重要，一个流行的评估框架基于治疗效果解释比例（PTE）。虽然该领域的方法学进展主要集中在估计方法上，但解释仍然是一个挑战，阻碍了 PTE 的实际应用。我们回顾了各种解释 PTE 的方法。这些解释——两种因果解释和一种非因果解释——揭示了 PTE 主要替代指标、因果中介分析和试验水平治疗效果预测之间的联系。这些解释的一个共同局限性是依赖于无法验证的假设。因此，我们认为只有当研究人员愿意做出非常强的假设时，PTE 才有意义。这些挑战在对三个假设性疫苗试验的分析中也得到了说明。
