Score Estimating Equations from Embedded Likelihood Functions Under Accelerated Failure Time Model

• 成果类型: Article
• 署名作者: Ning, Jing; Qin, Jing; Shen, Yu
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.946034
• 发表日期: 2014
• 页码: 1625-1635
• 关键词: large-sample theory; linear rank-tests; empirical distributions; regression-analysis; prevalent cohort; survival; inference

摘要

The semiparametric accelerated failure time (AFT) model is one of the most popular models for analyzing time-to-event outcomes. One appealing feature of the AFT model is that the observed failure time data can be transformed to identically independent distributed random variables without covariate effects. We describe a class of estimating equations based on the score functions for the transformed data, which are derived from the full likelihood function under commonly used semiparametric models such as the proportional hazards or proportional odds model. The methods of estimating regression parameters under the AFT model can be applied to traditional right-censored survival data as well as more complex time-to-event data subject to length-biased sampling. We establish the asymptotic properties and evaluate the small sample performance of the proposed estimators. We illustrate the proposed methods through applications in two examples.