A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time

Statistics in Medicine

Volumn 33, Issue 4, 2014, Pages 580-594

  Luo, Sheng ^a  

^a UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Clinical trial; Failure time; Item response theory; Latent variable; Markov chain Monte Carlo

Indexed keywords

ARTICLE; BAYES THEOREM; CLINICAL STUDY; COMPUTER PROGRAM; CONTROLLED STUDY; DATA ANALYSIS; DATATOP STUDY; DISEASE COURSE; DISEASE SEVERITY; HUMAN; MONTE CARLO METHOD; MULTILEVEL ITEM RESPONSE THEORY; MULTIVARIATE LONGITUDINAL DATA; PARAMETRIC ACCELERATED FAILURE TIME; PROBABILITY; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL PARAMETERS;

CLINICAL TRIAL; FAILURE TIME; ITEM RESPONSE THEORY; LATENT VARIABLE; MARKOV CHAIN MONTE CARLO;

BAYES THEOREM; COMPUTER SIMULATION; DISEASE PROGRESSION; DRUG THERAPY, COMBINATION; HUMANS; LONGITUDINAL STUDIES; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; PARKINSON DISEASE; RANDOMIZED CONTROLLED TRIALS AS TOPIC; SELEGILINE; TOCOPHEROLS;

EID: 84892486309     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5956     Document Type: Article

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