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Parametric likelihood inference for interval censored competing risks data.

Michael G Hudgens1, Chenxi Li, Jason P Fine

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

Biometrics
|January 10, 2014
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Summary
This summary is machine-generated.

This study introduces parametric estimation methods for cumulative incidence functions (CIF) with interval-censored competing risks data. The proposed naive likelihood estimator performs well and is comparable in efficiency to full maximum likelihood estimators.

Keywords:
Competing risksCumulative incidence functionGompertzHIV/AIDSMaximum likelihood

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Competing risks data present challenges in survival analysis.
  • Interval censoring complicates standard survival estimation techniques.
  • Accurate estimation of cumulative incidence is crucial for understanding disease progression and treatment effects.

Purpose of the Study:

  • To adapt existing parametric models for cumulative incidence function (CIF) estimation to interval-censored competing risks data.
  • To evaluate the performance of maximum likelihood estimators and a novel naive likelihood estimator under interval censoring.
  • To apply these methods to real-world data from a mother-to-child HIV transmission prevention trial.

Main Methods:

  • Extension of parametric CIF models from right-censored to interval-censored competing risks data.
  • Development and comparison of full maximum likelihood estimators and a naive likelihood estimator.
  • Simulation studies to assess the performance and efficiency of the estimators.
  • Application to a randomized clinical trial dataset.

Main Results:

  • Both full maximum likelihood and naive likelihood estimators are proposed for interval-censored competing risks data.
  • The naive likelihood estimator is valid under mixed-case interval censoring and offers separate model estimation.
  • Simulation results indicate the naive estimator performs well and can be efficient compared to the full likelihood estimator.
  • The methods were successfully applied to HIV mother-to-child transmission prevention trial data.

Conclusions:

  • Parametric estimation of CIF is feasible and valuable for interval-censored competing risks data.
  • The naive likelihood estimator provides a practical and efficient alternative in certain scenarios.
  • These methods enhance the analysis of complex survival data in clinical research.