Using covariates to reduce uncertainty in the economic evaluation of clinical trial data

Health Economics

Volumn 14, Issue 6, 2005, Pages 545-557

  Vazquez Polo F J ^a   Negrin Hernandez M A ^a   Gonzalez Lopez Valcarcel B ^a  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

Author keywords

Bayesian analysis; Cost effectiveness; Gibbs sampling; Markov chain Monte Carlo (MCMC) methods; Uncertainty

Indexed keywords

ADULT; AGED; ARTICLE; BAYES THEOREM; CANCER PATIENT; CLINICAL PRACTICE; CLINICAL RESEARCH; CLINICAL STUDY; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; COST EFFECTIVENESS ANALYSIS; COVARIANCE; ECONOMIC EVALUATION; FEMALE; HEALTH CARE POLICY; HUMAN; MAJOR CLINICAL STUDY; MALE; MALIGNANT NEOPLASTIC DISEASE; MEDICAL ASSESSMENT; MEDICAL TECHNOLOGY; METHODOLOGY; PRIORITY JOURNAL; PROCESS MODEL; PROGNOSIS; PUBLISHING; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; TREATMENT OUTCOME;

BAYES THEOREM; CLINICAL TRIALS; COST-BENEFIT ANALYSIS; MARKOV CHAINS; MONTE CARLO METHOD; SPAIN;

EID: 20444420471     PISSN: 10579230     EISSN: None     Source Type: Journal    
DOI: 10.1002/hec.947     Document Type: Article

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