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Efficient inferences for linear transformation models with doubly censored data.

Sangbum Choi1, Xuelin Huang2

  • 1Department of Statistics, Korea University, Seoul 02841, South Korea.

Communications in Statistics: Theory and Methods
|January 29, 2025
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Summary

This study introduces a new method for analyzing medical data with double censoring, improving accuracy in HIV/AIDS trials. The nonparametric maximum likelihood estimation (NPMLE) offers efficient and reliable results for transformation models.

Keywords:
Case-1 censoringEmpirical processInterval-censoringNonparametric likelihoodProportional hazardsProportional oddsSelf-consistency

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Informatics

Background:

  • Medical studies, particularly HIV/AIDS clinical trials, frequently encounter doubly-censored data.
  • This data type includes both exact and interval-censored observations, posing analytical challenges.

Purpose of the Study:

  • To develop and evaluate a nonparametric maximum likelihood estimation (NPMLE) method for semiparametric transformation models under double censoring.
  • To provide a robust statistical framework for analyzing complex survival data in medical research.

Main Methods:

  • Direct maximization of a nonparametric likelihood function to estimate regression and nuisance parameters.
  • Utilizing the inverse of the observed information matrix for statistical inference.

Main Results:

  • The proposed NPMLE is consistent and asymptotically normal.
  • Simulation studies confirm the method's efficacy, even with heavy censoring, outperforming existing estimation function-based methods.
  • The method demonstrated superior efficiency in simulations.

Conclusions:

  • The NPMLE provides an efficient and reliable approach for semiparametric transformation models with doubly-censored data.
  • The method is applicable to real-world medical data, as shown in an AIDS clinical trial analysis.