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An additive hazards frailty model with semi-varying coefficients
Zhongwen Zhang1, Xiaoguang Wang2, Yingwei Peng3
1School of Public Health and Management, Binzhou Medical University, Yantai, 264003, China.
Lifetime Data Analysis
|November 25, 2021
Summary
This study introduces an additive hazards frailty model with semi-varying coefficients to analyze time-to-event data when proportional hazards assumptions fail. The model estimates time-varying and time-invariant effects for clustered and recurrent events.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Proportional hazards frailty models are common for clustered/recurrent failure time data.
- The proportional hazards assumption may not hold in real-world applications.
- Need for flexible models accommodating time-dependent covariate effects.
Purpose of the Study:
- Propose a novel additive hazards frailty model with semi-varying coefficients.
- Accommodate both time-invariant and time-varying covariate effects.
- Provide robust estimation methods for survival data analysis.
Main Methods:
- Developed an additive hazards frailty model with semi-varying coefficients.
- Employed estimating equations for time-varying and time-invariant coefficients.
- Utilized the moment method for frailty parameter estimation.
- Established large sample properties and conducted simulation studies.
Main Results:
- The proposed estimators demonstrate desirable large sample properties.
- Simulation studies confirm the finite sample performance of the estimators.
- The model effectively analyzes time-to-event data with complex covariate patterns.
Conclusions:
- The additive hazards frailty model with semi-varying coefficients offers a flexible alternative to proportional hazards models.
- The proposed estimation methods are statistically sound and practically applicable.
- Demonstrated utility in analyzing colorectal cancer rehospitalization data.
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