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 tumour 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.

翻译：在竞争风险分析中，治疗组间的协变量不平衡使得比较累积发生率曲线变得困难。本文讨论了估计调整后累积发生率曲线的不同方法，包括治疗逆概率加权和结局回归建模。这些方法要发挥作用，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，它仅要求两个模型中有一个被正确设定。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/或结局之间关系存在模型误设的情景。我们在一项乳腺癌患者队列研究中展示了这些方法的应用，估计了接受乳房切除术治疗或保乳治疗后复发、第二原发肿瘤发生及死亡的协变量调整边际累积发生率曲线。我们的研究指出了在竞争风险分析中应用每种协变量调整方法的优缺点。