Estimation of sensitivity and specificity of multiple repeated binary tests without a gold standard

被引：7
作者：
Wang, Chunling [1 ]
Hanson, Timothy E. [2 ]
机构：
[1] Univ South Carolina, Dept Stat, Columbia, SC 29208 USA
[2] Medtronic Inc, Minneapolis, MN USA
来源：
STATISTICS IN MEDICINE | 2019年 / 38卷 / 13期
关键词：
latent class analysis; repeated binary outcomes; test accuracy; DIAGNOSTIC OUTCOME DATA; ERROR RATES; CONDITIONAL DEPENDENCE; DISEASE PREVALENCE; MODEL; ABSENCE; IDENTIFIABILITY; ACCURACY;
D O I：
10.1002/sim.8114
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
A model for multiple diagnostic tests, applied repeatedly over time on each subject, is proposed; gold standard data are not required. The model is identifiable with as few as three tests, and correlation among tests at each time point in the diseased and nondiseased populations, as well as across time points, is explicitly included. An efficient Markov chain Monte Carlo scheme allows for straightforward posterior inference; sample R code is available in the Supporting Web Materials for this paper. The proposed model is broadly illustrated via simulations and an analysis of scaphoid fracture data from a prospective study. In addition, omnibus tests constructed from individual tests in parallel and serial are considered.
引用
收藏
页码：2381 / 2390
页数：10
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