  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.
