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Marginal semiparametric accelerated failure time cure model for clustered survival data.

Yi Niu1, Duze Fan1, Jie Ding1

  • 1School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China.

Statistical Methods in Medical Research
|December 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing survival data in groups, like patients with correlated outcomes. The method effectively handles potential long-term survivors and correlated data, offering robust estimation for complex survival analysis.

Keywords:
Clustered survival dataaccelerated failure time modelefficiencygeneralized estimating equationmarginal methodmixture cure model

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Traditional cure models often assume independent data, limiting their application to clustered or correlated survival data.
  • Extending semiparametric accelerated failure time mixture cure models to clustered data presents estimation complexities.
  • Existing methods lack robust approaches for analyzing failure time data with potential cure fractions in clustered settings.

Purpose of the Study:

  • To propose a marginal semiparametric accelerated failure time mixture cure model for clustered right-censored failure time data.
  • To develop a novel and practical estimation method for this complex model.
  • To address the challenge of analyzing survival data where individuals within clusters may have correlated outcomes and a proportion may be cured.

Main Methods:

  • Developed a generalized estimating equations (GEE) approach combined with the expectation-maximization (EM) algorithm for parameter estimation.
  • Modeled within-cluster correlation structures using working correlation matrices within the GEE framework.
  • Established the large sample properties of the proposed regression estimators.

Main Results:

  • The proposed estimation method is user-friendly and robust to the misspecification of working correlation matrices.
  • Higher estimation efficiency is achieved when the assumed working correlation structure closely matches the true correlation.
  • The model and method were successfully applied to a contralateral breast cancer study, yielding new insights.

Conclusions:

  • The developed marginal semiparametric accelerated failure time mixture cure model and GEE-EM estimation method provide a valuable tool for analyzing clustered survival data with cure fractions.
  • The approach effectively accounts for within-subject correlation, leading to more accurate and efficient analysis.
  • This methodology offers new perspectives in analyzing correlated survival data, as demonstrated in the breast cancer study.