Survival data with competing or semi‐competing risks are common in observational studies. As an alternative to cause‐specific and subdistribution hazard ratios, the between‐group difference in cause‐specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease‐free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)‐free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non‐CVD death over 10 years among individuals with AF.

中文翻译：

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存在竞争风险时的受限平均时间损失的估计和建模

具有竞争或半竞争风险的生存数据在观察性研究中很常见。作为特定原因和子分布风险比的替代方法，特定原因限制平均损失时间 (RMTL) 的组间差异给出了因特定死因或无疾病损失时间损失的预期寿命的平均差异，在非致命结果的情况下，在预先指定的时期内。为了调整协变量，我们为 RMTL 中的边际差异引入了逆概率加权估计量及其方差。我们还为 RMTL 引入了审查加权回归模型的逆概率。在模拟研究中，我们检查了所提出的方法在比例和非比例子分布危险情景下的有限样本性能。我们使用来自弗雷明汉心脏研究的竞争风险数据说明了这两种方法。我们估计了 40 年来无房颤 (AF) 时间的性别差异。我们还估计了 AF 患者在 10 年内死于心血管疾病 (CVD) 和非 CVD 死亡的平均寿命的性别差异。