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Inference for the dependent competing risks model with masked causes of failure
Radu V Craiu1, Benjamin Reiser
1Department of Statistics, University of Toronto, 100 St. George Street, Toronto, Ontario, M5S 3G3, Canada. craiu@utstat.toronto.edu
Lifetime Data Analysis
|April 4, 2006
Summary
This study introduces an EM-based approach for dependent competing risks, improving statistical inference when failure causes are unknown. It provides estimators for sub-distribution functions, even with masked failure data.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Competing risks models analyze multiple failure causes.
- Traditional methods assume independent failure causes.
- Masked failure causes complicate analysis.
Purpose of the Study:
- To propose an EM-based approach for dependent competing risks.
- To develop estimators for sub-distribution functions with masked causes.
- To address parameter identifiability in masked failure scenarios.
Main Methods:
- Utilized an Expectation-Maximization (EM) algorithm.
- Modeled cause-specific hazard rates as piecewise constant functions.
- Developed estimators for sub-distribution functions.
Main Results:
- The proposed EM approach handles dependent competing risks.
- Estimators for sub-distribution functions were produced.
- Identifiability of parameters was discussed for masked data.
Conclusions:
- The EM-based method effectively addresses dependent competing risks.
- The approach is valuable for analyzing masked failure data.
- The methods were validated using two real-world datasets.
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