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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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What price semiparametric Cox regression?
Martin Jullum1, Nils Lid Hjort2
1Department of Mathematics, University of Oslo, Oslo, Norway. jullum@nr.no.
Lifetime Data Analysis
|September 16, 2018
Summary
This study compares parametric and semiparametric Cox models for censored survival data. New information criteria (FIC and AFIC) are developed to help select the most appropriate model for specific research goals.
Area of Science:
- Biostatistics
- Survival Analysis
Background:
- Cox's proportional hazards model is standard for censored life-time data.
- The standard model is semiparametric with an unspecified baseline hazard.
- Fully parametric Cox models offer potential efficiency gains if the baseline is correctly specified.
Purpose of the Study:
- Compare asymptotic relative efficiencies of parametric vs. semiparametric models.
- Develop model selection criteria (FIC and AFIC) for practical application.
- Extend methodology to compare non-parametric estimators (Kaplan-Meier, Nelson-Aalen) with parametric models.
Main Methods:
- Asymptotic relative efficiency comparisons between model types.
- Development of Focused Information Criteria (FIC) and Averaged FIC (AFIC).
- Application to real-world censored survival data.
Main Results:
- Efficiency gains from parametric models vary significantly depending on the quantity being estimated.
- FIC and AFIC provide tools for selecting appropriate proportional hazards models.
- Methodology validated through real data applications and theoretical analysis.
Conclusions:
- Model choice between parametric and semiparametric approaches impacts efficiency in survival data analysis.
- FIC and AFIC offer practical solutions for model selection in proportional hazards regression.
- The developed methods are applicable to both covariate and no-covariate scenarios in survival analysis.
Keywords:
Cox regressionFocused information criteriaModel selectionParametrics and semiparametricsSurvival dataMore Related Videos
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