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SEMIPARAMETRIC TRANSFORMATION MODELS WITH RANDOM EFFECTS FOR CLUSTERED FAILURE TIME DATA.

Donglin Zeng1, D Y Lin, Xihong Lin

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7420, U.S.A.

Statistica Sinica
|October 8, 2009
PubMed
Summary

We introduce flexible semiparametric transformation models with random effects for analyzing correlated failure times. Our methods are statistically sound and perform well in practice, offering robust tools for survival data analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Correlated failure times are common in medical research, arising from clustered data or repeated events.
  • Existing transformation models may not fully capture the complexity of time-dependent covariates and random effects in such data.

Purpose of the Study:

  • To propose a general class of semiparametric transformation models incorporating random effects.
  • To accommodate time-dependent covariates and various random-effects distributions for clustered or correlated failure times.

Main Methods:

  • Developed a flexible class of semiparametric transformation models with random effects.
  • Utilized nonparametric maximum likelihood estimators for model parameter estimation.
  • Established consistency, asymptotic normality, and asymptotic efficiency of the estimators.
  • Formulated likelihood-based inference procedures.

Main Results:

  • The proposed models encompass common transformation models like proportional hazards and proportional odds.
  • The methods accommodate various random-effects distributions, including Gaussian.
  • Nonparametric maximum likelihood estimators demonstrated desirable statistical properties.
  • Simulation studies confirmed the practical utility and performance of the proposed methods.

Conclusions:

  • The proposed semiparametric transformation models with random effects provide a powerful and flexible framework for analyzing correlated failure time data.
  • The developed statistical inference procedures are sound and validated through simulations and a real-world case study.
  • These methods offer advancements in modeling complex survival data, particularly with time-dependent covariates.