Methods for evaluating the performance of diagnostic tests in the absence of a gold standard: a latent class model approach

被引：65
|
作者：
Garrett, ES
Eaton, WW
Zeger, S
机构：
[1] Johns Hopkins Univ, Sch Med, Oncol Ctr, Div Biostat, Baltimore, MD 21205 USA
[2] Johns Hopkins Univ, Sch Hyg & Publ Hlth, Dept Mental Hyg, Baltimore, MD USA
[3] Johns Hopkins Univ, Sch Hyg & Publ Hlth, Dept Biostat, Baltimore, MD 21205 USA
来源：
STATISTICS IN MEDICINE | 2002年 / 21卷 / 09期
关键词：
latent class models; validation; depression; operating characteristics; diagnostic criteria; Markov chain Monte Carlo;
D O I：
10.1002/sim.1105
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In many areas of medical research, `gold standard' diagnostic tests do not exist and so evaluating the performance of standardized diagnostic criteria or algorithms is problematic. In this paper we propose an approach to evaluating the operating characteristics of diagnoses using a latent class model. By defining `true disease' as our latent variable, we are able to estimate sensitivity, specificity and negative and positive predictive values of the diagnostic test. These methods are applied to diagnostic criteria for depression using Baltimore's Epidemiologic Catchment Area Study Wave 3 data. Copyright (C) 2002 John Wiley Sons, Ltd.
引用
收藏
页码：1289 / 1307
页数：19
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