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：协变量在治疗组间的不平衡使得在竞争风险分析中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，包括治疗加权逆概率法和结局回归建模法。这些方法需要分别正确指定倾向评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，它仅需正确指定其中一个模型即可。通过模拟研究评估了这三种方法的性能，包括协变量与治疗和/或结局关系模型设定错误的情景。我们在乳腺癌患者队列研究中展示了其应用，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。本研究指出了每种协变量调整方法在竞争风险分析中的优缺点。