title: Accelerated failure time model under general biased sampling scheme.
abstract: Right-censored time-to-event data are sometimes observed from a (sub)cohort of patients whose survival times can be subject to outcome-dependent sampling schemes. In this paper, we propose a unified estimation method for semiparametric accelerated failure time models under general biased estimating schemes. The proposed estimator of the regression covariates is developed upon a bias-offsetting weighting scheme and is proved to be consistent and asymptotically normally distributed. Large sample properties for the estimator are also derived. Using rank-based monotone estimating functions for the regression parameters, we find that the estimating equations can be easily solved via convex optimization. The methods are confirmed through simulations and illustrated by application to real datasets on various sampling schemes including length-bias sampling, the case-cohort design and its variants.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
authors.forename: Jane Paik
authors.initials: JP
authors.lastname: Kim
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR tonysit@sta.cuhk.edu.hk.
authors.forename: Tony
authors.initials: T
authors.lastname: Sit
authors.affiliation: Department of Statistics, Columbia University, New York, NY 10027, USA.
authors.forename: Zhiliang
authors.initials: Z
authors.lastname: Ying
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design; D015983:Selection Bias; D016019:Survival Analysis
keywords: Accelerated failure time model; Case–cohort design; Counting process; Estimating equations; Importance sampling; Length-bias; Regression; Survival data