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A comparative simulation study for estimating accelerated failure time models
Journal of the Korean Data & Information Science Society 2018;29:1457-68
Published online November 30, 2018
© 2018 Korean Data and Information Science Society.

Sangbum Choi1

1Department of Statistics, Korea University
Correspondence to: Assistant professor, Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea. Email: choisang@korea.ac.kr
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (2017R1C1B1004817) and the grant from Korea University (K1822621).
Received October 1, 2018; Revised November 14, 2018; Accepted November 15, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Semiparametric accelerated failure time (AFT) models directly relate the predicted failure times to covariates and are a useful alternative to Cox's proportional hazards models that work on the hazard function or the survival function. In this paper, we briefly review different approaches to estimate the AFT model and evaluate their performance with nite samples via extensive simulation studies. Speci cally, we compared (i) inverse probability of censoring weighted (IPCW) least squares, (ii) log-rank estimator, (iii) Gehan-type log-rank estimator, (iv) Buckley-James estimator, and (v) nonparametric maximum likelihood estimator (NPMLE). Overall, rank-based estimators and Buckley-James estimator are efficient and relatively more robust to distributions of residual and censoring variables, whereas the IPCW estimator is very sensitive to distribution and amount of censoring. The NPMLE is asymptotically efficient and useful as it allows for hazard-based formulation, and thus can be used to analyze more structured survival data.
Keywords : Linear model, rank regression, relative efficiency, survival analysis.