Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package IC-Trans-CFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.
Publication:
Biometrics, Volume 80, Issue 3, September 2024
https://doi.org/10.1093/biomtc/ujae078
Author:
Hongxi Li
School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China
Shuwei Li
School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China
Email: seslishuw@gzhu.edu.cn
Liuquan Sun
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China
Xinyuan Song
Department of Statistics, Chinese University of Hong Kong, Hong Kong, 999077, China
