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Published on: October 23, 2020
Regression analysis of multivariate interval-censored failure time data with application to tumorigenicity
Xingwei Tong1, Man-Hua Chen, Jianguo Sun
1School of Mathematical Sciences, Beijing Normal University, Beijing, PR China 100875. xweitong@bnu.edu.cn
Biometrical Journal. Biometrische Zeitschrift
|April 26, 2008
Summary
This study introduces a new method for analyzing correlated survival data when exact failure times are unknown. The approach effectively handles interval-censored failure time data in complex experiments.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Multivariate interval-censored failure time data arise when multiple correlated survival times are of interest, but only observed within intervals.
- Such data are common in fields like tumorigenicity experiments, involving various tumor types or locations.
Purpose of the Study:
- To develop a marginal inference approach for analyzing multivariate interval-censored failure time data.
- To apply this approach to bivariate interval-censored data from tumorigenicity experiments using the additive hazards model.
Main Methods:
- Development of a marginal inference approach for multivariate interval-censored data.
- Application of the additive hazards model for regression analysis.
- Utilizing bivariate interval-censored data from a tumorigenicity experiment.
Main Results:
- The proposed marginal inference approach demonstrates good performance for practical situations.
- Simulation studies confirm the approach's effectiveness in handling interval-censored failure time data.
Conclusions:
- The developed marginal inference approach provides a robust method for analyzing complex survival data.
- This method is particularly useful for applications like tumorigenicity experiments with interval-censored outcomes.
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