Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Biometrical Journal

Volumn 58, Issue 3, 2016, Pages 549-569

  Chan, Jennifer S K ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Bayesian analysis; Conditional and joint model; Informative dropout; Longitudinal binary data; Selection model

Indexed keywords

DROPS; PARAMETER ESTIMATION; RANDOM PROCESSES;

BAYESIAN; BAYESIAN ANALYSIS; CONDITIONAL MODELS; DROP-OUT; INFORMATIVE DROPOUT; JOINT MODELS; LONGITUDINAL BINARY DATA; PARAMETERS ESTIMATES; RANDOM EFFECTS; SELECTION MODEL;

BAYESIAN NETWORKS;

BAYES THEOREM; CLINICAL STUDY; HUMAN; LONGITUDINAL STUDY; PATIENT DROPOUT; PROBABILITY; PROCEDURES; REGRESSION ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

BAYES THEOREM; CLINICAL STUDIES AS TOPIC; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; PATIENT DROPOUTS; PROBABILITY; REGRESSION ANALYSIS;

EID: 84952019391     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201400064     Document Type: Article

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