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Fitting Cox Models with Doubly Censored Data Using Spline-Based Sieve Marginal Likelihood.

Zhiguo Li1, Kouros Owzar1

  • 1Department of Biostatistics and Bioinformatics, Duke University.

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|May 31, 2016
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Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing interval-censored failure time data, improving accuracy in survival analysis for complex event times.

Keywords:
doubly censored datamultiple imputationproportional hazards modelspline-based marginal likelihood

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Interval-censored data, where event times are known only to fall within a time interval, presents unique analytical challenges.
  • Accurate modeling of failure times is crucial in various fields, including medicine and reliability engineering.
  • Existing methods may struggle with the complexity of doubly interval-censored data, where both originating and failure event times are interval censored.

Purpose of the Study:

  • To propose a novel statistical approach for fitting Cox proportional hazards models to interval-censored failure time data.
  • To develop a method that efficiently handles the integration of originating event times within the likelihood function.
  • To enhance the estimation of the baseline hazard function for improved survival analysis.

Main Methods:

  • Utilizing a spline-based sieve maximum marginal likelihood estimation for Cox proportional hazards models.
  • Integrating out the time to the originating event in the empirical likelihood function for the failure time of interest.
  • Incorporating the time to the originating event as a covariate to model its dependence on the failure time.

Main Results:

  • The proposed method significantly reduces the complexity of the objective function compared to fully semiparametric likelihood approaches.
  • The use of splines accelerates the convergence rate of the baseline hazard function estimator.
  • A multiple imputation approach facilitates computational efficiency.

Conclusions:

  • The developed spline-based sieve maximum marginal likelihood method provides an effective and computationally efficient solution for analyzing interval-censored failure time data.
  • The method demonstrates superior performance in estimating the baseline hazard function.
  • The approach is validated through asymptotic theory, simulation studies, and application to AIDS incubation time data.