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Leveraging External Aggregated Information for the Marginal Accelerated Failure Time Model.

Ping Xie1, Jie Ding1, Xiaoguang Wang1

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

Statistics in Medicine
|October 8, 2024
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Summary
This summary is machine-generated.

Researchers can improve correlated survival data analysis by integrating external covariate information. This unified framework enhances estimation efficiency and provides robust methods for heterogeneous populations and uncertain auxiliary data.

Keywords:
auxiliary informationclustered survival datageneralized method of momentslinear regressionweighted rank‐estimating equation

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Correlated survival data analysis is crucial in epidemiology.
  • Existing methods often focus on univariate data, limiting external information integration.
  • Small-scale studies can benefit from leveraging auxiliary data for enhanced analysis.

Purpose of the Study:

  • To propose a unified framework for improved estimation of marginal accelerated failure time models with correlated survival data.
  • To integrate external covariate information from reduced models into the analysis.
  • To enhance efficiency and robustness of survival data analysis.

Main Methods:

  • Developed a unified framework using generalized method of moments to combine internal and external data.
  • Proposed an estimator integrating covariate effects from a reduced model.
  • Introduced a shrinkage estimator for population heterogeneity and refined procedures for uncertain auxiliary information.

Main Results:

  • The proposed estimator is asymptotically more efficient than conventional methods using internal data alone.
  • The shrinkage estimator mitigates bias and efficiency loss in heterogeneous populations.
  • The refined procedure improves inference reliability with uncertain auxiliary information.

Conclusions:

  • The unified framework effectively enhances the estimation of marginal accelerated failure time models with correlated survival data.
  • The proposed methods offer improved efficiency and robustness, particularly in the presence of population heterogeneity and uncertain external information.
  • Empirical application confirms the practical relevance of the developed statistical methods.