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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks
Bella Vakulenko-Lagun1, Colin Magdamo2, Marie-Laure Charpignon3
1Department of Statistics, University of Haifa, Haifa, Israel.
Computer Methods and Programs in Biomedicine
|September 29, 2023
Summary
This study introduces causalCmprsk, an R package for analyzing competing risks data in clinical studies. It helps estimate treatment effects accurately, avoiding bias from multiple event types and confounding factors.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Causal Inference
Background:
- Competing risks are common in time-to-event clinical studies.
- Ignoring competing risks can lead to biased conclusions.
- Confounding bias must be addressed in observational treatment effect estimation.
Purpose of the Study:
- Develop an R package, causalCmprsk, for estimating average treatment effects in the presence of competing risks.
- Provide tools for causal analysis of time-to-event outcomes with multiple, mutually exclusive event paths.
Main Methods:
- Implemented inverse probability weighting to emulate randomization and reduce selection bias.
- Adapted existing survival analysis methods for causal analysis in non-parametric and semi-parametric frameworks.
- Offers different weighting strategies for various target populations.
Main Results:
- The package includes two main functions: `fit.cox` (semiparametric Cox model) and `fit.nonpar` (non-parametric).
- Provides estimators for absolute risks (cumulative incidence) and relative treatment effects (hazard ratios, restricted mean time differences).
- Restricted mean time differences quantify treatment effects in an intuitive time domain.
Conclusions:
- The causalCmprsk package is a valuable tool for causal analysis of competing risks data.
- Facilitates distinguishing between different causes of follow-up termination.
- Offers accessible, time-varying treatment effect measures with detailed documentation.
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