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A Class of Additive Transformation Models for Recurrent Gap Times
Ling Chen1, Yanqin Feng2, Jianguo Sun3
1Division of Biostatistics, Washington University School of Medicine, Campus Box 8067, 660 S. Euclid Ave, St. Louis, MO 63110, U.S.A.
Communications in Statistics: Theory and Methods
|March 26, 2021
Summary
This study introduces two new methods, modified within-cluster resampling (MWCR) and weighted risk-set (WRS), for analyzing recurrent event gap times in medical research. These methods offer consistent and efficient estimation, even with complex correlations.
Area of Science:
- Biostatistics
- Survival Analysis
- Medical Statistics
Background:
- Recurrent events are common in longitudinal studies, particularly in medical research.
- Analyzing the time gap between these events is crucial for understanding disease progression and treatment efficacy.
- Existing methods may struggle with the inherent correlations in recurrent event data.
Purpose of the Study:
- To develop novel regression analysis methods for recurrent event gap times.
- To address the challenge of arbitrary correlations among gap times.
- To provide consistent and asymptotically normal estimators for regression models.
Main Methods:
- Proposing two novel estimation procedures: modified within-cluster resampling (MWCR) and weighted risk-set (WRS).
- Developing regression analysis for a general class of additive transformation models.
- Utilizing closed-form estimators for ease of computation.
Main Results:
- The proposed MWCR and WRS estimators are shown to be consistent and asymptotically normal.
- The methods effectively handle arbitrary correlations among gap times.
- Simulation studies confirm good finite sample performance.
Conclusions:
- The MWCR and WRS methods provide robust and practical approaches for analyzing recurrent event gap times.
- These methods are applicable to real-world clinical trial data, such as in chronic granulomatous disease (CGD) studies.
- The developed techniques enhance the statistical toolkit for longitudinal medical research.
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