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Multivariate survival analysis for case-control family data.

Li Hsu1, Malka Gorfine

  • 1Program in Biostatistics and Biomathematics, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B500, PO Box 19024, Seattle, WA 98109-1024, USA. lih@fhcrc.org

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This study introduces a frailty-based multivariate survival model to analyze correlated disease onset ages in family studies. The method effectively estimates regression and correlation parameters for breast cancer family data.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Multivariate survival data are common in family studies, where disease onset ages among relatives are often correlated.
  • Existing models may not fully capture these complex dependencies.

Purpose of the Study:

  • To develop and validate a frailty-based multivariate survival model for correlated family data.
  • To estimate regression and correlation parameters within a proportional hazards framework.

Main Methods:

  • Utilized a frailty-based approach to model correlated ages at disease onset in family members.
  • Employed nonparametric estimation of the baseline hazard function via the innovation theorem.
  • Obtained maximum pseudolikelihood estimators for regression and correlation parameters.

Main Results:

  • The proposed method effectively estimates parameters in multivariate survival data.
  • Demonstrated a connection between the frailty-based approach and generalized estimating equations.
  • Simulation studies confirmed the methodology's performance.

Conclusions:

  • The frailty-based multivariate survival model is a practical tool for analyzing correlated familial disease onset.
  • The methodology provides robust estimation for regression and correlation parameters in complex family structures.
  • Applicable to epidemiological studies, such as familial breast cancer analysis.