Modeling sensitivity and specificity with a time-varying reference standard within a longitudinal setting

被引：2
|
作者：
Yu, Qin [1 ]
Tang, Wan [1 ,2 ]
Marcus, Sue [3 ]
Ma, Yan [4 ]
Zhang, Hui [1 ]
Tu, Xin [1 ,2 ]
机构：
[1] Univ Rochester, Dept Biostat & Computat Biol, Rochester, NY 14623 USA
[2] Univ Rochester, Dept Psychiat, Rochester, NY 14623 USA
[3] Mt Sinai Sch Med, Dept Psychiat, New York, NY 10029 USA
[4] Cornell Univ, Weill Med Coll, Dept Publ Hlth, New York, NY 10021 USA
来源：
JOURNAL OF APPLIED STATISTICS | 2010年 / 37卷 / 07期
关键词：
augmented inverse probability weighted (AIPW) estimate; bivariate monotone missing data pattern (BMMDP); diagnostic test; double robust estimate; inverse probability weighted (IPW) estimate; missing data; COLLABORATIVE-COCAINE-TREATMENT; BAYESIAN-INFERENCE; NATIONAL-INSTITUTE; SELF-REPORTS; ACCURACY; PERFORMANCE; PREVALENCE; SCORE;
D O I：
10.1080/02664760902998444
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Diagnostic tests are used in a wide range of behavioral, medical, psychosocial, and healthcare-related research. Test sensitivity and specificity are the most popular measures of accuracy for diagnostic tests. Available methods for analyzing longitudinal study designs assume fixed gold or reference standards and as such do not apply to studies with dynamically changing reference standards, which are especially popular in psychosocial research. In this article, we develop a novel approach to address missing data and other related issues for modeling sensitivity and specificity within such a time-varying reference standard setting. The approach is illustrated with real as well as simulated data.
引用
收藏
页码：1213 / 1230
页数：18
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