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Inferences on the association parameter in copula models for bivariate survival data
Biometrics
|December 1, 1995
Summary
We developed new statistical methods for analyzing survival data with missing information. These two-stage estimators for association parameters in copula models are efficient and robust, even with censored data.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Copula models are essential for analyzing bivariate survival data, capturing dependency structures.
- Censoring in survival data is common and presents analytical challenges.
- Estimating the association parameter accurately is crucial for reliable bivariate survival analysis.
Purpose of the Study:
- To develop and evaluate two-stage parametric and semi-parametric estimation procedures for the association parameter in bivariate survival data.
- To assess the asymptotic properties and simulation performance of these estimators.
- To propose a consistent variance estimator for the semi-parametric approach.
Main Methods:
- Two-stage estimation procedures (parametric and semi-parametric) were developed.
- Asymptotic properties of the estimators were derived.
- Simulation studies were conducted to compare estimator performance.
- A consistent variance estimator for the semi-parametric approach was proposed.
Main Results:
- Both parametric and semi-parametric estimators demonstrated efficiency at independence.
- Marginal parameter estimates exhibited high efficiency and robustness to dependency misspecification.
- The proposed methods were successfully applied to an AIDS data set.
Conclusions:
- The developed two-stage methods provide efficient and robust estimation of association parameters in bivariate survival data with censoring.
- The proposed variance estimator enhances the reliability of semi-parametric estimates.
- These methods offer valuable tools for analyzing complex survival data in biostatistics and related fields.
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