Random effects models in latent class analysis for evaluating accuracy of diagnostic tests

Biometrics

Volumn 52, Issue 3, 1996, Pages 797-810

  Qu, Yinsheng ^a   Tan, Ming ^a   Kutner, Michael H ^a  

^a LERNER RESEARCH INSTITUTE   (United States)

Author keywords

Bootstrap; Conditional independence; EM algorithm; Finite mixture models; Item response theory; Sensitivity; Specificity

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIAGNOSIS; RANDOM PROCESSES;

ACCURACY OF DIAGNOSTIC TESTS; BOOTSTRAP; CONDITIONAL DEPENDENCE; CONDITIONAL INDEPENDENCES; EM ALGORITHMS; FINITE MIXTURE MODELING; ITEM RESPONSE THEORY; RANDOM EFFECTS MODEL; SENSITIVITY; SPECIFICITY;

GRAPHIC METHODS;

ALGORITHM; ARTICLE; BIOMETRY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST; EVALUATION AND FOLLOW UP; METHODOLOGY; RANDOMIZATION; STATISTICAL MODEL; STATISTICS;

EID: 0029795558     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.2307/2533043     Document Type: Article

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