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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Vertical modelling: Analysis of competing risks data with missing causes of failure.
M A Nicolaie1, H C van Houwelingen2, H Putter2
1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. M.A.Nicolaie@lumc.nl.
Statistical Methods in Medical Research
|December 20, 2011
Summary
Vertical modeling offers a straightforward method for analyzing competing risks data with missing failure types. This approach effectively uses all data for all-cause hazards and specific data for relative hazards.
Area of Science:
- Biostatistics
- Epidemiology
- Survival Analysis
Background:
- Competing risks analysis is crucial in medical research.
- Missing failure types in competing risks data present analytical challenges.
- Existing methods may not fully utilize available data.
Purpose of the Study:
- To introduce and validate vertical modeling for competing risks data with missing failure types.
- To demonstrate how vertical modeling naturally incorporates missing data assumptions.
- To compare vertical modeling with existing methods.
Main Methods:
- Utilizing the observed data likelihood under a missing-at-random assumption.
- Estimating parameters using vertical modeling.
- Comparing vertical modeling with the Goetghebeur and Ryan method.
- Applying the methods to a breast cancer dataset.
Main Results:
- Vertical modeling identifies all-cause and relative hazards as key likelihood quantities.
- The method efficiently uses all individuals for all-cause hazard estimation.
- Individuals with known failure types are used for relative hazard estimation.
- Demonstrated practical implications for analyzing incomplete competing risks data.
Conclusions:
- Vertical modeling provides a simple and attractive approach for competing risks analysis with missing failure types.
- The method aligns with statistical principles and offers practical advantages.
- Vertical modeling offers a valuable alternative for handling missing data in survival analysis.
- Comparative analysis highlights the distinct contributions of different methods.
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