A general model-checking procedure for semiparametric accelerated failure time models

• Published in: Statistics and computing 2024-06, Vol.34 (3), Article 117
• Main Authors: Choi, Dongrak, Bae, Woojung, Yan, Jun, Kang, Sangwook
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Asymptotic properties; Computer Science; Empirical analysis; Error detection; Failure times; Gaussian process; Goodness of fit; Original Paper; Probability and Statistics in Computer Science; Statistical tests; Statistical Theory and Methods; Statistics and Computing/Statistics Programs; Survival analysis
• ISSN: 0960-3174; 1573-1375
• DOI: 10.1007/s11222-024-10431-7

We propose a set of goodness-of-fit tests for the semiparametric accelerated failure time (AFT) model, including an omnibus test, a link function test, and a functional form test. This set of tests is derived from a multi-parameter cumulative sum process shown to follow asymptotically a zero-mean Gaussian process. Its evaluation is based on the asymptotically equivalent perturbed version, which enables both graphical and numerical evaluations of the assumed AFT model. Empirical p -values are obtained using the Kolmogorov-type supremum test, which provides a reliable approach for estimating the significance of both proposed un-standardized and standardized test statistics. The proposed procedure is illustrated using the rank-based estimator but is general in the sense that it is directly applicable to some other popular estimators such as induced smoothed rank-based estimator or least-squares estimator that satisfies certain properties. Our proposed methods are rigorously evaluated using extensive simulation experiments that demonstrate their effectiveness in maintaining a Type I error rate and detecting departures from the assumed AFT model in practical sample sizes and censoring rates. Furthermore, the proposed approach is applied to the analysis of the Primary Biliary Cirrhosis data, a widely studied dataset in survival analysis, providing further evidence of the practical usefulness of the proposed methods in real-world scenarios. To make the proposed methods more accessible to researchers, we have implemented them in the R package afttest , which is publicly available on the Comprehensive R Archive Network.