ArticleStatistics in medicine2025
Doubly Robust Estimation of Marginal Cumulative Incidence Curves for Competing Risk Analysis.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Multiply-robust estimator of cumulative incidence function difference for right-censored competing risks data.BMC medical research methodology · 2026Article
- Doubly Robust Estimation of Marginal Cumulative Incidence Curves for Competing Risk Analysis.Statistics in medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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