title: Score Estimating Equations from Embedded Likelihood Functions under Accelerated Failure Time Model.
abstract: 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.
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
authors: Jing
authors: J
authors: Ning
authors: Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, NIH Bethesda, Maryland 20892, USA.
authors: Jing
authors: J
authors: Qin
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
authors: Yu
authors: Y
authors: Shen
keywords: Accelerated failure time model; Cox model; Length-biased data; Likelihood function; Proportional odds model; Score equation