Semiparametric Estimation of an Accelerated Failure Time Model with Time- Dependent Covariates

Biometrika ◽  
1992 ◽  
Vol 79 (2) ◽  
pp. 311 ◽  
Author(s):  
James Robins ◽  
Anastosios A. Tsiatis
Keyword(s):  
Failure Time ◽  
Semiparametric Estimation ◽  
Time Dependent ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Time Model ◽  
Accelerated Failure ◽  
Time Dependent Covariates ◽  
Failure Time Model

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

2021 ◽  
pp. 096228022110417
Author(s):  
Menglan Pang ◽  
Robert W Platt ◽  
Tibor Schuster ◽  
Michal Abrahamowicz
Keyword(s):  
Prognostic Factors ◽  
Proportional Hazards ◽  
Failure Time ◽  
Cox Proportional Hazards Model ◽  
Time Dependent ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Time Model ◽  
Accelerated Failure ◽  
Failure Time Model

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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