Bootstrap test for proportional hazard assumption on time-independent systematic effects of longevity data

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Abstract

This manuscript focuses on the development of a bootstrap test for validating the proportional hazard (PH) assumption in longevity data, avoiding parametric assumptions on baseline survival and hazard patterns, and subjective interpretations of previously developed graphical tests. Monte Carlo simulations are used to generate new data sets from the estimated Kaplan-Meier survival function, and inferences are then made on the coefficient of variation (CV) of the estimated hazard over time. One-tailed bootstrap intervals can be established, given that the CV could theoretically range between 0 (perfect PH) and +∞ (absolute loss of proportionality between hazard functions). This procedure was tested by simulation, and the obtained results suggested it as a useful statistical tool when Kaplan-Meier assumptions are satisfied. If not, this bootstrap test was robust for medium to large data sets, whereas it could suffer from statistical biases when testing small populations. © 2011 Blackwell Verlag GmbH.
Original languageEnglish
Pages (from-to)100-104
JournalJournal of Animal Breeding and Genetics
Volume128
Issue number2
DOIs
Publication statusPublished - 1 Apr 2011

Keywords

  • Bootstrap
  • Coefficient of variation
  • Proportional hazard
  • Survival analysis

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