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Investigating covariate-by-centre interaction in survival data.

L Biard1,2,3, M Labopin4,5,6,7, S Chevret1,2,3

  • 11 Service de Biostatistique et Information Médicale, AP-HP Hôpital Saint-Louis, Paris, France.

Statistical Methods in Medical Research
|May 12, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new permutation test for survival analysis, effectively detecting center effects in multicenter studies. The method accurately identifies variations in baseline hazard and covariate effects across centers, enhancing clinical trial reliability.

Keywords:
Centre effectCox modelpermutation testproportional hazardsrandom effectssurvival

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • Multicenter studies frequently assess center effects on survival outcomes.
  • Mixed-effects models are used to investigate heterogeneity in baseline hazard and covariate effects.
  • Existing methods may lack routine procedures for testing multiple random effects.

Purpose of the Study:

  • To develop and validate a procedure for routinely testing multiple random effects in survival analyses.
  • To assess center-specific variations in baseline hazard and covariate effects within a Cox model framework.
  • To provide a robust statistical tool for analyzing heterogeneity in multicenter clinical trials.

Main Methods:

  • Proposed a statistic and permutation approach to test for non-zero components in the variance-covariance matrix of random effects.
  • Utilized a mixed-effects Cox model framework.
  • Evaluated test performance via simulations under various null and alternative hypotheses.

Main Results:

  • Permutation tests demonstrated valid Type I error rates under null hypotheses.
  • The proposed method achieved satisfactory statistical power across different alternative hypotheses.
  • Simulations confirmed the reliability of testing multiple random effects in survival models.

Conclusions:

  • The developed permutation test procedure is effective for detecting multiple random effects in survival analyses.
  • This approach reliably identifies center effects on baseline hazard and covariate impacts.
  • The method was successfully applied to real-world data from European cohorts of stem cell transplants for acute leukaemia.