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Updated: Jul 29, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Estimation of competing risks with general missing pattern in failure types.
Anup Dewanji1, Debasis Sengupta
1Applied Statistics Unit, Indian Statistical Institute, 203, B. T. Road, Calcutta 700 035, India. dewanjia@isical.ac.in
Biometrics
|February 19, 2004
Summary
This study addresses missing failure types in competing risks data. New statistical methods, including expectation maximization and a Nelson-Aalen type estimator, effectively handle missing data, showing small bias even with high missingness.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Missing failure types are common in competing risks data.
- Accurate analysis requires robust methods to handle this missingness.
Purpose of the Study:
- To develop and evaluate statistical methods for analyzing competing risks data with missing failure types.
- To address general missing patterns where a set of possible types is observed.
Main Methods:
- Maximum likelihood estimation using the expectation-maximization (EM) algorithm under a missing-at-random assumption.
- A novel Nelson-Aalen type estimator based on a least-squares method, utilizing conditional probabilities of true types.
Main Results:
- Simulation studies indicate small bias for the proposed methods, even with a high proportion of missing data and sufficient observations.
- Estimates show sensitivity to misspecification of conditional probabilities when missingness is high.
Conclusions:
- The proposed methods offer effective solutions for competing risks data with missing failure types.
- Careful consideration of conditional probability specification is crucial, especially with substantial missing data.
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