# Doubly Robust Estimation of Marginal Cumulative Incidence Curves for Competing Risk Analysis

## TL;DR

This paper introduces a new method to adjust for covariate imbalance in comparing treatment outcomes in competing risk scenarios, such as breast cancer recurrence and death.

## Contribution

The paper introduces a doubly robust estimator that requires correct specification of only one model for accurate results.

## Key findings

- The doubly robust estimator performs well even when one model is misspecified.
- Simulation studies show the advantages and limitations of different adjustment methods in competing risk analysis.
- The methods are applied to a breast cancer cohort to estimate adjusted cumulative incidence curves.

## Abstract

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.