Comparing proportional hazards and accelerated failure time models for survival analysis

Abstract

This paper describes a method proposed for a censored linear regression model that can be used in the context of survival analysis. The method has the important characteristic of allowing estimation and inference without knowing the distribution of the duration variable. Moreover, it does not need the assumption of proportional hazards. Therefore, it can be an interesting alternative to the Cox proportional hazards models when this assumption does not hold. In addition, implementation and interpretation of the results is simple. In order to analyse the performance of this methodology, we apply it to two real examples and we carry out a simulation study. We present its results together with those obtained with the traditional Cox model and AFT parametric models. The new proposal seems to lead to more precise results.

Determination of Predictors Associated With HIV/AIDS Patients on ART Using Accelerated Failure Time Model for Interval Censored Survival Data

“…Orbe et al (2002), suggested that the AFT models; lognormal and loglogistic should be treated as an alternative choice when proportional hazard does not hold. Moreover it is too difficult and complicated to obtain the estimates in Cox PH model for interval censored data.…”

“…AFTM is a linear regression model in which the response variable is logarithm or a known monotone transformation of a failure time (Kalbfleish and Prentice, 1980). AFTM has been extensively studied by Buckely and James (1979;Koul et al, 1981;Robins and Tsiatis, 1992;Jin et al, 2003;Kwong and Hutton, 2003;Orbe et al, 2002). Kay and Kinnersley (2002), suggested that AFTM is an alternative choice, when the proportional hazard assumptions does not hold.…”

Predictive Factors of Survival Time of Breast Cancer in Kurdistan Province of Iran between 2006-2014: A Cox Regression Approach

“…Prognostic factors of breast cancer have already identified by using non-parametric survival methods such as Kaplan-Meier and Cox proportional hazard (PH) in many studies (Moran et al, 2008Akhsan andAryandono, 2010;Khodabakhshi et al, 2011), the latter is used when the effect of covariates on the hazard ratio is desired. Review of literature shows the extensive use of the Cox PH regression model for hazard rate or instantaneous risk of a given event (Orbe et al, 2002;Moran et al, 2008). However, the basis and the most important assumption underlying this model is the proportionality of hazard rates, which may not be held in some situations.…”

“…Where PH assumption is not met, it is improper to use standard Cox PH model as it may entail serious bias and loss of power when estimating or making inference about the effect of a given prognostic factor on mortality (Moran et al, 2008). A review of survival analysis in cancer journals reveals that only 5% of all studies using Cox PH model considered the underlying assumption (Orbe et al, 2002).…”

Comparison between Parametric and Semi-parametric Cox Models in Modeling Transition Rates of a Multi-state Model: Application in Patients with Gastric Cancer Undergoing Surgery at the Iran Cancer Institute

“…The absence of proportional hazards assumption causes the estimations of transition rates among different states to be unreliable and biased. Moreover, studies conducted in this scope demonstrate that either proportional hazards assumption is made or not, parametric models are more efficient (Orbe et al, 2002;Patel et al, 2006). Therefore, parametric models such as exponential, Weibull, log-normal, log-logistic, Gompertz and gamma can be better choices in such situations.…”