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Semiparametric regression of panel count data with informative terminal event
Xiangbin Hu1, L I Liu2, Ying Zhang3
1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China.
Bernoulli : Official Journal of the Bernoulli Society for Mathematical Statistics and Probability
|April 30, 2025
Summary
This study introduces a robust statistical model for analyzing recurrent events in panel count data, accounting for informative terminal events. The method effectively estimates event rates, even with complex data structures.
Area of Science:
- Biostatistics
- Survival Analysis
- Longitudinal Data Analysis
Background:
- Panel count data analysis is crucial for understanding recurrent events.
- Informative terminal events can bias traditional statistical models.
- Robust methods are needed to accurately analyze complex health data.
Purpose of the Study:
- To develop a semiparametric model for robust analysis of panel count data.
- To explicitly model the effect of an informative terminal event on recurrent events.
- To provide accurate estimation and asymptotic properties for the proposed model.
Main Methods:
- A conditional mean model for a reversed counting process anchored at the terminal event.
- A predicted least squares-based two-stage estimation procedure.
- Spline-based sieve estimation techniques to handle nuisance parameters.
Main Results:
- The proposed estimator achieves a derived convergence rate.
- Asymptotic normality is established for finite-dimensional and infinite-dimensional parameters.
- The method demonstrates robustness in simulation studies.
Conclusions:
- The developed semiparametric model offers a robust approach for panel count data with informative terminal events.
- The methodology provides reliable estimation and valid statistical inference.
- The approach is applicable to real-world health studies, such as aging research.
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