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Partial likelihood ratio tests for the Cox model under complex sampling.

Thomas Lumley1, Alastair Scott

  • 1Department of Statistics, University of Auckland, Auckland, New Zealand. t.lumley@auckland.ac.nz

Statistics in Medicine
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

Researchers developed a likelihood ratio test analog for Cox proportional hazards models using survey data. This method improves small-sample performance and is illustrated with real-world health survey examples.

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Area of Science:

  • Biostatistics
  • Survey Methodology
  • Survival Analysis

Background:

  • Cox proportional hazards models are widely used for survival data analysis.
  • Standard statistical tests may not perform optimally with complex sample survey data.
  • Accurate statistical inference is crucial for analyzing health and epidemiological data from surveys.

Purpose of the Study:

  • To develop and evaluate a likelihood ratio test analog for Cox models applied to sample survey data.
  • To investigate methods for computing the asymptotic distribution of the proposed test.
  • To enhance the small-sample performance of statistical tests in survey data analysis.

Main Methods:

  • Development of a likelihood ratio test analog tailored for survey data.
  • Exploration of computational techniques for determining the asymptotic distribution.
  • Assessment of methods to improve small-sample statistical properties.

Main Results:

  • The proposed likelihood ratio test analog provides a viable approach for Cox models with survey data.
  • Methods for computing the asymptotic distribution were successfully applied.
  • Techniques for improving small-sample performance were identified and demonstrated.

Conclusions:

  • The developed likelihood ratio test analog is suitable for analyzing Cox proportional hazards models with sample survey data.
  • The study offers practical solutions for statistical inference challenges in complex survey designs.
  • Illustrative examples using the National Health and Nutrition Examination Survey data validate the proposed methodology.