4.6 Article

Flexible extension of the accelerated failure time model to account for nonlinear and time-dependent effects of covariates on the hazard

Journal

STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 30, Issue 11, Pages 2526-2542

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/09622802211041759

Keywords

Accelerated failure time model; survival analysis; time-dependent effect; nonlinear effect; regression splines; simulations

Funding

  1. Fonds de Recherche du Quebec-Sante (FRQS)
  2. Canada Research Chair in Biostatistical Methods for Primary Health Care Research (Tier II)
  3. NSERC

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The accelerated failure time model is an alternative to the Cox proportional hazards model in survival analysis, but requires meeting specific underlying modeling assumptions for valid conclusions. This model lacks formal investigation of the time ratio and linearity assumptions, while prognostic factors may have time-dependent and nonlinear effects.
The accelerated failure time model is an alternative to the Cox proportional hazards model in survival analysis. However, conclusions regarding the associations of prognostic factors with event times are valid only if the underlying modeling assumptions are met. In contrast to several flexible methods for relaxing the proportional hazards and linearity assumptions in the Cox model, formal investigation of the constant-over-time time ratio and linearity assumptions in the accelerated failure time model has been limited. Yet, in practice, prognostic factors may have time-dependent and/or nonlinear effects. Furthermore, parametric accelerated failure time models require correct specification of the baseline hazard function, which is treated as a nuisance parameter in the Cox proportional hazards model, and is rarely known in practice. To address these challenges, we propose a flexible extension of the accelerated failure time model where unpenalized regression B-splines are used to model (i) the baseline hazard function of arbitrary shape, (ii) the time-dependent covariate effects on the hazard, and (iii) nonlinear effects for continuous covariates. Simulations evaluate the accuracy of the time-dependent and/or nonlinear estimates, and of the resulting survival functions, in multivariable settings. The proposed flexible extension of the accelerated failure time model is applied to re-assess the effects of prognostic factors on mortality after septic shock.

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