A fast Monte Carlo expectation-maximization algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with atypical glandular cells

In this article, we use a latent class model (LCM) with prevalence modeled as a function of covariates to assess diagnostic test accuracy in situations where the true disease status is not observed, but observations on three or more conditionally independent diagnostic tests are available. A fast Mo...

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Vydané v:	Journal of applied statistics Ročník 40; číslo 12; s. 2699 - 2719
Hlavní autori:	Kang, Le, Carter, Randy, Darcy, Kathleen, Kauderer, James, Liao, Shu-Yuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Taylor & Francis 01.12.2013 Taylor & Francis Ltd
Predmet:	Accuracy adjusted information matrix Algorithms bootstrap standard errors Comparative studies Computer simulation diagnostic accuracy Diagnostic systems Estimates Gynecology imperfect gold standard latent class model Mathematical models MCEM estimation Medical diagnosis Monte Carlo methods Monte Carlo simulation Parameter estimation Standard error
ISSN:	0266-4763, 1360-0532
On-line prístup:	Získať plný text
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