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On proportional hazards assumption under the random effects models

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Abstract

The proportional hazards mixed-effects model (PHMM) was proposed to handle dependent survival data. Motivated by its application in genetic epidemiology, we study the interpretation of its parameter estimates under violations of the proportional hazards assumption. The estimated fixed effect turns out to be an averaged regression effect over time, while the estimated variance component could be unaffected, inflated or attenuated depending on whether the random effect is on the baseline hazard, and whether the non-proportional regression effect decreases or increases over time. Using the conditional distribution of the covariates we define the standardized covariate residuals, which can be used to check the proportional hazards assumption. The model checking technique is illustrated on a multi-center lung cancer trial.

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Authors and Affiliations

  1. Division of Biostatistics and Bioinformatics, Department of Family and Preventive Medicine, University of California, San Diego, CA, 92093-0112, USA

    Ronghui Xu & Anthony Gamst

  2. Department of Mathematics, University of California, San Diego, CA, 92093-0112, USA

    Ronghui Xu

Authors
  1. Ronghui Xu
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  2. Anthony Gamst
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Correspondence to Ronghui Xu.

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Xu, R., Gamst, A. On proportional hazards assumption under the random effects models. Lifetime Data Anal 13, 317–332 (2007). https://doi.org/10.1007/s10985-007-9041-5

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  • DOI: https://doi.org/10.1007/s10985-007-9041-5

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