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Models for association in bivariate survival data.

¹Department of Mathematics, Miyazaki University, Japan.

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|July 1, 1990

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This study reviews bivariate survival data dependence models, focusing on the mixture model. A new Fujii test for marginal distribution equality under Weibull hazards is compared to existing methods via simulation.

Area of Science:

Statistics
Survival Analysis
Biostatistics

Background:

Bivariate survival data analysis is crucial in many fields.
Existing dependence models include shock, Freund, Clayton, and mixture models.
Testing equality of marginal distributions is a key problem.

Purpose of the Study:

To review and classify dependence models for bivariate survival data.
To focus on the mixture model and the problem of testing marginal distribution equality.
To introduce and evaluate a new test proposed by Fujii.

Main Methods:

Review of existing dependence models for bivariate survival data.
Focus on mixture models with Weibull type baseline hazard.
Introduction of Fujii's new test.

Related Experiment Videos

Simulation study comparing Fujii's test with Nayak's test and the sign test.

Main Results:

The paper provides a classification of bivariate survival data dependence models.
The Fujii test's characteristics are investigated through simulation.
Performance comparison of Fujii's test against Nayak's and the sign test is conducted.

Conclusions:

The study contributes to the understanding of dependence modeling in bivariate survival analysis.
The simulation results offer insights into the performance of the new Fujii test.
This research aids in selecting appropriate statistical tests for marginal distribution equality.