title: A Unified Approach to Semiparametric Transformation Models under General Biased Sampling Schemes.
abstract: We propose a unified estimation method for semiparametric linear transformation models under general biased sampling schemes. The new estimator is obtained from a set of counting process-based unbiased estimating equations, developed through introducing a general weighting scheme that offsets the sampling bias. The usual asymptotic properties, including consistency and asymptotic normality, are established under suitable regularity conditions. A closed-form formula is derived for the limiting variance and the plug-in estimator is shown to be consistent. We demonstrate the unified approach through the special cases of left truncation, length-bias, the case-cohort design and variants thereof. Simulation studies and applications to real data sets are presented.
authors.affiliation: Department of Medicine, Stanford University, Stanford CA 94305.
authors.forename: Jane Paik
authors.initials: JP
authors.lastname: Kim
authors.forename: Wenbin
authors.initials: W
authors.lastname: Lu
authors.forename: Tony
authors.initials: T
authors.lastname: Sit
authors.forename: Zhiliang
authors.initials: Z
authors.lastname: Ying
keywords: Case-cohort design; Counting process; Cox model; Estimating equations; Importance sampling; Length-bias; Proportional odds model; Regression; Survival data; Truncation