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Dynamic semiparametric transformation models for recurrent event data with a terminal event
Jin Jin1, Xinyuan Song2, Liuquan Sun3,4
1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China.
Statistics in Medicine
|September 19, 2022
Summary
This study introduces dynamic semiparametric models for recurrent events and terminal events in longitudinal studies. The methods allow for time-varying covariate effects, improving analysis of complex health data.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Survival Analysis
Background:
- Recurrent event data with a terminal event are common in longitudinal studies.
- Existing models may not fully capture time-varying covariate effects.
Purpose of the Study:
- To propose dynamic semiparametric transformation models for recurrent events and terminal events.
- To develop methods for estimating model parameters and testing time-varying covariate effects.
Main Methods:
- Dynamic semiparametric transformation models.
- Estimation procedures for model parameters.
- Significance tests for time-varying covariate effects.
- Model checking procedures.
Main Results:
- Asymptotic properties of estimators are established.
- Simulation studies demonstrate the performance of proposed estimators.
- An application to a medical cost study is presented.
Conclusions:
- The proposed models provide a flexible framework for analyzing recurrent events with a terminal event.
- The methods are effective in handling time-varying covariate effects.
- The approach is applicable to real-world health studies, such as chronic heart failure patient costs.
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