Applications of continuous time hidden Markov models to the study of misclassified disease outcomes

Statistics in Medicine

Volumn 22, Issue 3, 2003, Pages 441-462

  Bureau, Alexandre ^a   Shiboski, Stephen ^b   Hughes, James P ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Hidden Markov models; Infectious diseases; Longitudinal data; Misclassification

Indexed keywords

ACICLOVIR; ANTIFUNGAL AGENT; ANTIVIRUS AGENT; ZIDOVUDINE;

ADOLESCENT; ADULT; ALGORITHM; ANALYTICAL ERROR; ANAMNESIS; ARTICLE; CLINICAL OBSERVATION; COHORT ANALYSIS; CONTROLLED STUDY; COVARIANCE; DATA ANALYSIS; DISEASE CLASSIFICATION; DISEASE COURSE; DRUG CLEARANCE; FEMALE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; LEUKOPLAKIA; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL COMPUTING; MAXIMUM LIKELIHOOD METHOD; PARAMETRIC TEST; PROBABILITY; PROSPECTIVE STUDY; RECURRENT INFECTION; REGRESSION ANALYSIS; STATISTICAL MODEL; UTERINE CERVICITIS; VIRUS INFECTION; WART VIRUS;

ADOLESCENT; ADULT; ANTIVIRAL AGENTS; DISEASE PROGRESSION; FEMALE; HIV INFECTIONS; HUMANS; LEUKOPLAKIA, HAIRY; MALE; MARKOV CHAINS; MODELS, BIOLOGICAL; PAPILLOMAVIRUS INFECTIONS; PROBABILITY; SAN FRANCISCO; TUMOR VIRUS INFECTIONS;

EID: 0037441443     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1270     Document Type: Article

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