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Related Experiment Videos

Linear regression for bivariate censored data via multiple imputation.

W Pan1, C Kooperberg

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, Box 303, 420 Delaware Street SE, Minneapolis, MN 55455-0378, USA. weip@biostat.umn.edu

Statistics in Medicine
|November 2, 1999
PubMed
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This study introduces improved methods for analyzing bivariate survival data, crucial for twin studies and paired organ research. Accounting for the correlation between survival times significantly enhances the efficiency of regression analysis.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Bivariate survival data are common in twin studies and analyses of paired organs (e.g., eyes, ears).
  • Regression analysis of survival times on predictors is often of interest in these contexts.
  • Existing methods for univariate censored data need extension for bivariate scenarios.

Purpose of the Study:

  • To extend Wei and Tanner's multiple imputation approach for linear regression to handle bivariate censored data.
  • To develop and compare methods for censored bivariate linear regression.
  • To improve the efficiency of regression analysis for correlated survival times.

Main Methods:

  • Formulated a class of censored bivariate linear regression methods using iterative imputation and model fitting.

Related Experiment Videos

  • Implemented three distinct methods: a marginal (independence) approach and two methods accounting for correlation.
  • The correlation-aware methods utilized generalized least squares regression and bivariate log-spline density estimation.

    • Main Results:

    • Simulation studies demonstrated that methods accounting for dependence between survival times are more efficient than the marginal approach.
    • The two proposed methods that consider correlation showed similar performance.
    • The developed methods were successfully applied to a clinical trial dataset for otitis media.

    Conclusions:

    • Accounting for the correlation between bivariate survival times is essential for efficient regression analysis.
    • The proposed generalized least squares and bivariate log-spline methods offer improved efficiency over marginal approaches.
    • These advanced statistical techniques provide valuable tools for analyzing complex survival data in various research fields.