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Tests of independence for censored bivariate failure time data
1Department of Statistics, North Carolina State University, Box 8203, Raleigh, USA. wlu4@stat.ncsu.edu
Lifetime Data Analysis
|January 16, 2007
Summary
This study introduces new chi-squared type tests for analyzing paired failure times in survival analysis, accounting for covariates. These tests offer a robust method for assessing independence in bivariate failure time data.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Bivariate failure time data is common in twin studies and medical research.
- Assessing independence between paired failure times, especially with covariates, is crucial.
Purpose of the Study:
- To develop and validate chi-squared type tests for independence in bivariate failure time data.
- To propose a flexible bivariate accelerated failure time model that does not specify dependence structures.
Main Methods:
- A class of chi-squared type tests for bivariate failure time independence.
- A bivariate accelerated failure time model with unspecified dependence.
- Resampling techniques for variance estimation of test statistics.
Main Results:
- Theoretical properties of the proposed tests were derived.
- Simulation studies demonstrated the practical applicability of the tests.
- The methodology was illustrated using real-world examples.
Conclusions:
- The proposed tests provide a reliable method for analyzing bivariate failure time data.
- The flexible model allows for unspecified dependence structures, enhancing applicability.
- The approach is validated for practical use in medical and epidemiological studies.
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