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20200106 钱静:Nonidentifiability in the Presence of Factorization for Truncated Data
时间:2020-01-03

报告时间:2020 / 01 / 06(周一)14:00-15:00
报告地点:明德主楼 1016 会议室

报告主题:Nonidentifiability in the Presence of Factorization for Truncated Data


报告摘要:Truncation is a structured form of selection bias that arises frequently in cohort studies. A time to event, X, is left-truncated by T if X can be observed only if T<X. This often results in oversampling of large values of X, and necessitates adjustment of estimation procedures to avoid bias. Simple risk-set adjustments can be made to standard risk-set-based estimators to accommodate left truncation when T and X are “quasi-independent”, i.e., independent in the observable region. Through examination of the likelihood function, we derive a weaker factorization condition for the conditional distribution of T given X in the observable region that permits risk-set adjustment for estimation of the distribution of X, but not of the distribution of T. Quasi-independence results when the analogous factorization condition for X given T holds also, in which case the distributions of X and T are easily estimated. While we can test for factorization, if the test does not reject, we cannot identify which factorization condition holds, or whether quasi-independence holds. Hence we require an unverifiable assumption in order to estimate the distribution of X or T based on truncated data. This contrasts with the common understanding that truncation is different from censoring in requiring no unverifiable assumptions for estimation. We illustrate these concepts through examples and a simulation study.


个人简介:钱静,中国人民大学统计学专业本科, 美国埃默里大学(Emory University) 生物统计专业博士,曾在美国哈佛大学(Harvard University)生物统计系进行博士后研究。现为美国麻省大学(University of Massachusetts, Amherst)生物统计与流行病学系副教授。研究领域为复杂抽样中的生存分析、分位数回归、自变量删失下的回归分析、生物标记分析与疾病风险预测。在Biometrika, Biometrics, Journal of the Royal Statistical Society, JAMA Neurology, PLOS Medicine, Cancer Research等高水平统计学、医学期刊上发表论文40余篇。现任国际数理统计学会学术期刊Annals of Applied Statistics副主编。