期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 108, 期 501, 页码 217-227出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2012.746073
关键词
Case-cohort design; Counting process; Cox model; Estimating equations; Importance sampling; Length bias; Proportional odds model; Regression; Survival data; Truncation
资金
- National Science Foundation
- National Institutes of Health
- Sir Edward Youde Memorial Fund
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 datasets are presented.
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