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NONPARAMETRIC ESTIMATION AND TESTING FOR PANEL COUNT DATA WITH INFORMATIVE TERMINAL EVENT
Xiangbin Hu1, Li Liu1, Ying Zhang1
1The Hong Kong Polytechnic University, Wuhan University and University of Nebraska Medical Center.
Statistica Sinica
|April 28, 2025
Summary
This study introduces a new statistical model for analyzing recurrent event data with terminal events. The proposed method provides robust and interpretable results for long-term follow-up studies.
Area of Science:
- Biostatistics
- Survival Analysis
- Longitudinal Data Analysis
Background:
- Recurrent event data analysis is crucial in long-term studies.
- Terminal events can significantly impact recurrent event processes.
- Existing models may not adequately address these complexities.
Purpose of the Study:
- To propose a novel reversed nonparametric mean model for panel count data with terminal events.
- To provide a statistically robust and interpretable framework for analyzing such data.
- To develop and evaluate new statistical tests for two-sample comparisons.
Main Methods:
- Developed a reversed nonparametric mean model for panel count data.
- Employed a two-stage estimation procedure combining Kaplan-Meier and nonparametric sieve estimation.
- Constructed new statistics for two-sample hypothesis testing.
Main Results:
- Established consistency, convergence rate, and asymptotic normality of the proposed estimator.
- Demonstrated the asymptotic properties of the new two-sample test statistics.
- Successfully applied the method to analyze panel count data from a real-world study.
Conclusions:
- The proposed model offers a robust and interpretable approach for recurrent event data with terminal events.
- The developed statistical tests are asymptotically valid and perform well in simulations.
- The method is effective for analyzing complex longitudinal health data.
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