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A class of Box-Cox transformation models for recurrent event data
Liuquan Sun1, Xingwei Tong, Xian Zhou
1Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China.
Lifetime Data Analysis
|April 15, 2010
Summary
This study introduces flexible Box-Cox transformation models for recurrent event data analysis. These models effectively capture covariate effects on mean functions, offering a robust approach for statistical modeling.
Area of Science:
- Biostatistics
- Statistical Modeling
- Survival Analysis
Background:
- Recurrent event data presents unique statistical challenges.
- Existing models may lack flexibility in capturing covariate effects.
- The proportional means model is a common but limited approach.
Purpose of the Study:
- To propose a flexible class of Box-Cox transformation models for recurrent event data.
- To extend the capabilities of proportional means models.
- To provide a robust framework for analyzing complex recurrent event data.
Main Methods:
- Development of Box-Cox transformation models for recurrent event data.
- Application of a profile pseudo-partial likelihood method for parameter estimation.
- Utilizing estimating equation approaches for inference.
- Conducting simulation studies to assess performance.
Main Results:
- The proposed models offer significant flexibility in modeling covariate effects on mean functions.
- The profile pseudo-partial likelihood method provides reliable parameter estimation.
- Large sample properties of estimators are established.
- Simulation studies demonstrate good performance in moderate sample sizes.
Conclusions:
- The proposed Box-Cox transformation models provide a flexible and powerful tool for recurrent event data analysis.
- The methodology is applicable to various fields, including clinical studies.
- Model checking procedures enhance the reliability of the analysis.
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