Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
550030Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Stat Methods Med Res. 2022 Oct;31(10):1881-1903. doi: 10.1177/09622802221102625. Epub 2022 May 23.
In the context of competing risks data, the subdistribution hazard ratio has limited clinical interpretability to measure treatment effects. An alternative is the difference in restricted mean times lost (RMTL), which gives the mean time lost to a specific cause of failure between treatment groups. In non-randomized studies, the average causal effect is conventionally used for decision-making about treatment and public health policies. We show how the difference in RMTL can be estimated by contrasting the integrated cumulative incidence functions from a Fine-Gray model. We also show how the difference in RMTL can be estimated by using inverse probability of treatment weighting and contrasts between weighted non-parametric estimators of the area below the cumulative incidence. We use pseudo-observation approaches to estimate both component models and we integrate them into a doubly-robust estimator. We demonstrate that this estimator is consistent when either component is correctly specified. We conduct simulation studies to assess its finite-sample performance and demonstrate its inherited consistency property from its component models. We also examine the performance of this estimator under varying degrees of covariate overlap and under a model misspecification of nonlinearity. We apply the proposed method to assess biomarker-treatment interaction in subpopulations of the POPLAR and OAK randomized controlled trials of second-line therapy for advanced non-small-cell lung cancer.
在竞争风险数据的背景下，亚分布风险比对测量治疗效果的临床解释能力有限。另一种方法是受限平均时间损失（RMTL）的差异，它可以给出治疗组之间特定失败原因的平均时间损失。在非随机研究中，平均因果效应通常用于治疗和公共卫生政策的决策。我们展示了如何通过对比 Fine-Gray 模型的综合累积发生率函数来估计 RMTL 的差异。我们还展示了如何通过使用治疗的逆概率加权和加权累积发生率下的非参数估计量之间的差异来估计 RMTL 的差异。我们使用伪观测方法来估计两个组成模型，并将它们集成到双稳健估计器中。我们证明了当任何一个组件都正确指定时，该估计器都是一致的。我们进行了模拟研究来评估其有限样本性能，并从其组件模型中证明了其固有的一致性。我们还研究了该估计器在不同程度的协变量重叠和非线性模型失配下的性能。我们应用所提出的方法来评估 POPLAR 和 OAK 随机对照试验中生物标志物与治疗相互作用的亚组，这些试验是针对晚期非小细胞肺癌的二线治疗。
