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Accelerated failure time models for the analysis of competing risks

Authors
Choi, SangbumCho, Hyunsoon
Issue Date
9월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
Accelerated life testing; Cause-specific hazard; Cumulative incidence; Inverse probability weighting; Linear regression; Survival analysis
Citation
JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.48, no.3, pp.315 - 326
Indexed
SCIE
SCOPUS
KCI
Journal Title
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
Volume
48
Number
3
Start Page
315
End Page
326
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/62972
DOI
10.1016/j.jkss.2018.10.003
ISSN
1226-3192
Abstract
Competing risks are common in clinical cancer research, as patients are subject to multiple potential failure outcomes, such as death from the cancer itself or from complications arising from the disease. In the analysis of competing risks, several regression methods are available for the evaluation of the relationship between covariates and cause-specific failures, many of which are based on Cox's proportional hazards model. Although a great deal of research has been conducted on estimating competing risks, less attention has been devoted to linear regression modeling, which is often referred to as the accelerated failure time (AFT) model in survival literature. In this article, we address the use and interpretation of linear regression analysis with regard to the competing risks problem. We introduce two types of AFT modeling framework, where the influence of a covariate can be evaluated in relation to either a cause-specific hazard function, referred to as cause-specific AFT (CS AFT) modeling in this study, or the cumulative incidence function of a particular failure type, referred to as crude-risk AFT (CR-AFT) modeling. Simulation studies illustrate that, as in hazard-based competing risks analysis, these two models can produce substantially different effects, depending on the relationship between the covariates and both the failure type of principal interest and competing failure types. We apply the AFT methods to data from non-Hodgkin lymphoma patients, where the dataset is characterized by two competing events, disease relapse and death without relapse, and non-proportionality. We demonstrate how the data can be analyzed and interpreted, using linear competing risks regression models. (C) 2018 Published by Elsevier B.V. on behalf of The Korean Statistical Society.
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