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Updated: May 29, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Inference for cumulative incidence quantiles via parametric and nonparametric approaches
1Department of Statistics, University of Wisconsin, Madison, WI 53706, USA. leem5@mail.nih.gov
Statistics in Medicine
|September 8, 2011
Summary
This study introduces new methods for analyzing survival data with competing risks, focusing on quantiles of the cumulative incidence function. Parametric approaches offer improved accuracy, especially when the model is well-specified, outperforming nonparametric methods.
Area of Science:
- Biostatistics
- Survival Analysis
- Epidemiology
Background:
- Median survival time is common in survival analysis.
- Quantile analysis for competing risks data is less explored.
- Cumulative incidence functions are key for competing risks.
Purpose of the Study:
- Propose parametric inferences for quantiles of cumulative incidence functions.
- Develop parametric confidence intervals for these quantiles.
- Investigate simplified nonparametric inference methods.
Main Methods:
- Parametric modeling of cumulative incidence functions.
- Development of parametric confidence intervals.
- Comparison with a simplified nonparametric approach.
- Simulation studies for performance evaluation.
Main Results:
- Proposed parametric methods perform well in simulations.
- Parametric analyses show smaller mean square error when models are adequately specified.
- Comparison highlights advantages of parametric approaches under certain conditions.
Conclusions:
- The study provides effective parametric inference methods for competing risks quantiles.
- Parametric methods are recommended when model assumptions are reasonably met.
- Illustrates practical application using breast cancer clinical trial data.
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