ArticleComputer methods and programs in biomedicine2023
causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data.
Article in Computer methods and programs in biomedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 7 citations in OpenAlex.
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- Multiply-robust estimator of cumulative incidence function difference for right-censored competing risks data.BMC medical research methodology · 2026Article
- Neutrophil inflammation metrics are associated with the risk of future dementia in large data from NYU Langone Hospitals and the Veterans Health Administration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- CAUSALRLSTACK: adaptive balancing of deep representation and causal effect estimation with application to HIV-related health data.BioData mining · 2025Article
- The comparative effectiveness and safety of fluticasone-salmeterol via metered-dose versus dry powder inhalers for COPD: A new user cohort study.PLoS medicine · 2025Article
- Assessing treatment effects with adjusted restricted mean time lost in observational competing risks data.BMC medical research methodology · 2024Article
- Association of BCG Vaccine Treatment With Death and Dementia in Patients With Non-Muscle-Invasive Bladder Cancer.JAMA network open · 2023Article
- Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.Nature communications · 2022Article
- Electroencephalographic Abnormalities are Common in COVID-19 and are Associated with Outcomes.Annals of neurology · 2021Article
- Prolonged Intubation in Patients With Prior Cerebrovascular Disease and COVID-19.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
Abstract
BACKGROUND AND
objectiveCompeting risks data arise in both observational and experimental clinical studies with time-to-event outcomes, when each patient might follow one of the multiple mutually exclusive competing paths. Ignoring competing risks in the analysis can result in biased conclusions. In addition, possible confounding bias of the treatment-outcome relationship has to be addressed, when estimating treatment effects from observational data. In order to provide tools for estimation of average treatment effects on time-to-event outcomes in the presence of competing risks, we developed the R package causalCmprsk. We illustrate the package functionality in the estimation of effects of a right heart catheterization procedure on discharge and in-hospital death from observational data.
methodsThe causalCmprsk package implements an inverse probability weighting estimation approach, aiming to emulate baseline randomization and alleviate possible treatment selection bias. The package allows for different types of weights, representing different target populations. causalCmprsk builds on existing methods from survival analysis and adapts them to the causal analysis in non-parametric and semi-parametric frameworks.
resultsThe causalCmprsk package has two main functions: fit.cox assumes a semiparametric structural Cox proportional hazards model for the counterfactual cause-specific hazards, while fit.nonpar does not impose any structural assumptions. In both frameworks, causalCmprsk implements estimators of (i) absolute risks for each treatment arm, e.g., cumulative hazards or cumulative incidence functions, and (ii) relative treatment effects, e.g., hazard ratios, or restricted mean time differences. The latter treatment effect measure translates the treatment effect from probability into more intuitive time domain and allows the user to quantify, for example, by how many days or months the treatment accelerates the recovery or postpones illness or death.
conclusionsThe causalCmprsk package provides a convenient and useful tool for causal analysis of competing risks data. It allows the user to distinguish between different causes of the end of follow-up and provides several time-varying measures of treatment effects. The package is accompanied by a vignette that contains more details, examples and code, making the package accessible even for non-expert users.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.