Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
BARDS, Merck & Co., Inc., North Wales, Pennsylvania, USA.
Biom J. 2024 Mar;66(2):e2200165. doi: 10.1002/bimj.202200165.
Clinical trials involving novel immuno-oncology therapies frequently exhibit survival profiles which violate the proportional hazards assumption due to a delay in treatment effect, and, in such settings, the survival curves in the two treatment arms may have a crossing before the two curves eventually separate. To flexibly model such scenarios, we describe a nonparametric approach for estimating the treatment arm-specific survival functions which constrains these two survival functions to cross at most once without making any additional assumptions about how the survival curves are related. A main advantage of our approach is that it provides an estimate of a crossing time if such a crossing exists, and, moreover, our method generates interpretable measures of treatment benefit including crossing-conditional survival probabilities and crossing-conditional estimates of restricted residual mean life. Our estimates of these measures may be used together with efficacy measures from a primary analysis to provide further insight into differences in survival across treatment arms. We demonstrate the use and effectiveness of our approach with a large simulation study and an analysis of reconstructed outcomes from a recent combination therapy trial.
涉及新型免疫肿瘤疗法的临床试验经常表现出生存曲线，由于治疗效果的延迟，违反了比例风险假设，并且在这种情况下，两条治疗曲线可能会在最终分开之前先交叉。为了灵活地模拟这种情况，我们描述了一种非参数方法来估计治疗臂特异性生存函数，该方法将这两个生存函数约束为最多交叉一次，而不对生存曲线之间的关系做出任何其他假设。我们方法的一个主要优点是，如果存在交叉，则提供了交叉时间的估计值，而且，我们的方法生成了可解释的治疗效益度量，包括交叉条件生存概率和交叉条件受限剩余平均寿命估计值。我们可以将这些度量的估计值与主要分析中的疗效度量结合使用，以进一步深入了解治疗臂之间的生存差异。我们通过一项大型模拟研究和对最近的联合治疗试验中重建结果的分析来展示我们方法的使用和有效性。
