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Related Experiment Videos

Cohort case-control design and analysis for clustered failure-time data.

Shou-En Lu1, Mei-Cheng Wang

  • 1Division of Biometrics, University of Medicine and Dentistry of New Jersey, New Brunswick, New Jersey 08903, USA. lus2@umdnj.edu

Biometrics
|December 24, 2002
PubMed
Summary

This study introduces a new cohort case-control design for analyzing multiple health outcomes over time. The method efficiently estimates risk factors using a novel sampling technique for complex survival data.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Survival Analysis

Background:

  • Cohort case-control designs are efficient for studying disease risk factors in large populations.
  • Existing methods are limited to analyzing single outcomes (univariate failure-time data).
  • There is a need for designs applicable to multiple correlated outcomes over time (multivariate failure-time data).

Purpose of the Study:

  • To develop and validate a cohort case-control design for multivariate failure-time data.
  • To propose a risk set sampling method for selecting controls.
  • To enable estimation of regression parameters in marginal proportional hazards models for correlated outcomes.

Main Methods:

  • Developed a novel cohort case-control design for multivariate failure-time data.

Related Experiment Videos

  • Proposed a risk set sampling method to select controls from non-failures, matched by failure time.
  • Utilized a pseudolikelihood approach for parameter estimation in marginal proportional hazards models, allowing unspecified correlation structures within clusters.

    • Main Results:

    • The proposed method allows for the analysis of multivariate failure-time data within a cohort case-control framework.
    • Simulation studies demonstrated the effectiveness of the proposed estimator.
    • A bootstrap method was developed for robust statistical inference.

    Conclusions:

    • The new cohort case-control design effectively extends the analysis of risk factors to multivariate failure-time data.
    • This methodology provides a valuable tool for epidemiological research, particularly in complex intervention studies.
    • The approach was successfully illustrated using data from a vitamin A supplementation trial.