Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models

• 成果类型: Article
• 署名作者: Shen, Yu; Ning, Jing; Qin, Jing
• 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: 10.1198/jasa.2009.tm08614
• 发表日期: 2009
• 页码: 1192-1202
• 关键词: kaplan-meier statistics; large-sample theory; linear rank-tests; nonparametric-estimation; empirical distributions; regression parameters; prevalent cohort; survival

摘要

Right-censored time-to-event data are often observed from a cohort of prevalent cases that are subject to length-biased sampling. Informative right censoring of data from the prevalent cohort within the population often makes it difficult to model risk factors on the unbiased failure times for the general population. because the observed failure times are length biased. In this paper. we consider two classes of flexible semiparametric models: the transformation models and the accelerated failure time models, to assess covariate effects on the population failure times by modeling the length-biased times. We develop unbiased estimating equation approaches to obtain the consistent estimators of the regression coefficients. Large sample properties for the estimators are derived. The methods are confirmed through simulations and illustrated by application to data from a study of a prevalent cohort of dementia patients.