Comparison of Bayesian, classical, and heuristic approaches in identifying acute disease events in lung transplant recipients

Statistics in Medicine

Volumn 23, Issue 5, 2004, Pages 803-824

  Troiani, John S ^a,b   Carlin, Bradley P ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF MINNESOTA MEDICAL SCHOOL   (United States)

Author keywords

Bayesian modelling; Hierarchical model; Home monitoring; Lung transplant; Markov chain Monte Carlo (MCMC) methods; ROC

Indexed keywords

ACUTE DISEASE; ALGORITHM; ARTICLE; BAYES THEOREM; CHI SQUARE TEST; CLASSIFICATION; CLINICAL ARTICLE; CONTROLLED STUDY; COVARIANCE; DATA ANALYSIS; HUMAN; INTERMETHOD COMPARISON; LUNG DISEASE; LUNG TRANSPLANTATION; MEDICAL DOCUMENTATION; MEDICAL RECORD; PATIENT SELECTION; PREDICTION; RANDOMIZATION; RECEIVER OPERATING CHARACTERISTIC; RECIPIENT; REGRESSION ANALYSIS; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; SPIROMETRY; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; SYMPTOMATOLOGY; VALIDATION PROCESS;

ACUTE DISEASE; ALGORITHMS; BAYES THEOREM; HOME NURSING; HUMANS; LUNG TRANSPLANTATION; MARKOV CHAINS; MODELS, STATISTICAL; MONITORING, PHYSIOLOGIC; MONTE CARLO METHOD; SENSITIVITY AND SPECIFICITY; SPIROMETRY;

EID: 1442301849     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1651     Document Type: Article

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