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Robust Estimation of Additive Shared-Frailty Models for Recurrent Event Data With Dependent Censoring
Xin Chen1,2, Jieli Ding3, Liuquan Sun4,5
1School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China.
Statistics in Medicine
|October 7, 2025
Summary
This study introduces a robust method for analyzing medical data with recurrent events and dependent censoring. The new approach handles complex data dependencies without needing to specify exact structures, improving analysis accuracy.
Area of Science:
- Biostatistics
- Medical Statistics
- Survival Analysis
Background:
- Recurrent event data with dependent censoring is common in medical studies.
- Analyzing these data is challenging due to complex dependencies between events.
Purpose of the Study:
- To propose a robust estimation procedure for additive shared-frailty models.
- To accommodate censoring times dependent on recurrent and failure events.
Main Methods:
- Utilized additive shared-frailty models for recurrent event processes and failure times.
- Developed a robust estimation procedure for dependent censoring.
- Did not require specifying exact dependence structures or frailty distributions.
Main Results:
- The proposed estimation procedure is consistent and asymptotically normal.
- Simulation studies demonstrated good finite-sample performance.
- The method was illustrated using a hospitalization dataset.
Conclusions:
- The new method provides a robust way to analyze complex medical follow-up data.
- It offers flexibility by not requiring specific dependence structures or frailty distributions.
- The approach is practical for real-world medical research.
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