Boosting joint models for longitudinal and time-to-event data

Biometrical Journal

Volumn 59, Issue 6, 2017, Pages 1104-1121

  Waldmann, Elisabeth ^a   Taylor Robinson, David ^b   Klein, Nadja ^c   Kneib, Thomas ^c   Pressler, Tania ^d   Schmid, Matthias ^e   Mayr, Andreas ^a,e  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

^c UNIVERSITY OF GÖTTINGEN   (Germany)

^d Rigshospitalet   (Denmark)

^e UNIVERSITY OF BONN   (Germany)

Author keywords

Boosting; High dimensional data; Joint modeling; Longitudinal models; Time to event analysis; Variable selection

Indexed keywords

BAYESIAN NETWORKS; CLUSTERING ALGORITHMS; LEARNING SYSTEMS;

BOOSTING; CLINICAL STUDY; CYSTIC FIBROSIS; EVENT ANALYSIS; HIGH DIMENSIONAL DATA; JOINT MODELS; LONGITUDINAL MODELS; TIME TO EVENTS; TIME-TO-EVENT ANALYSE; VARIABLES SELECTIONS;

MAXIMUM PRINCIPLE;

BAYES THEOREM; BIOMETRY; CYSTIC FIBROSIS; HUMAN; LONGITUDINAL STUDY; MACHINE LEARNING; PROCEDURES; REGISTER; STATISTICAL MODEL; TIME FACTOR;

BAYES THEOREM; BIOMETRY; CYSTIC FIBROSIS; HUMANS; LIKELIHOOD FUNCTIONS; LONGITUDINAL STUDIES; MACHINE LEARNING; MODELS, STATISTICAL; REGISTRIES; TIME FACTORS;

EID: 85015990186     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201600158     Document Type: Article

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