van Hage Patrick, le Cessie Saskia, van Maaren Marissa C, Putter Hein, van Geloven Nan
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
Institute of Biology Leiden, Leiden University, Leiden, the Netherlands.
Stat Med. 2025 Aug;44(18-19):e70066. doi: 10.1002/sim.70066.
Covariate imbalance between treatment groups makes it difficult to compare cumulative incidence curves in competing risk analyses. In this paper, we discuss different methods to estimate adjusted cumulative incidence curves, including inverse probability of treatment weighting and outcome regression modeling. For these methods to work, correct specification of the propensity score model or outcome regression model, respectively, is needed. We introduce a new doubly robust estimator, which requires correct specification of only one of the two models. We conduct a simulation study to assess the performance of these three methods, including scenarios with model misspecification of the relationship between covariates and treatment and/or outcome. We illustrate their usage in a cohort study of breast cancer patients estimating covariate-adjusted marginal cumulative incidence curves for recurrence, second primary tumor development, and death after undergoing mastectomy treatment or breast-conserving therapy. Our study points out the advantages and disadvantages of each covariate adjustment method when applied in competing risk analysis.
治疗组之间的协变量不平衡使得在竞争风险分析中比较累积发病率曲线变得困难。在本文中，我们讨论了估计调整后累积发病率曲线的不同方法，包括治疗权重的逆概率法和结局回归建模。要使这些方法有效，分别需要正确设定倾向得分模型或结局回归模型。我们引入了一种新的双重稳健估计量，它只需要正确设定两个模型中的一个。我们进行了一项模拟研究，以评估这三种方法的性能，包括协变量与治疗和/或结局之间关系的模型误设情况。我们在一项乳腺癌患者队列研究中说明了它们的用法，该研究估计了接受乳房切除术或保乳治疗后复发、第二原发性肿瘤发生和死亡的协变量调整边际累积发病率曲线。我们的研究指出了每种协变量调整方法在竞争风险分析中应用时的优缺点。
