Joint latent class models for longitudinal and time-to-event data: A review

Statistical Methods in Medical Research

Volumn 23, Issue 1, 2014, Pages 74-90

  Proust Lima, Cécile ^a,b   Séne, Mbéry ^a,b   Taylor, Jeremy M G ^c   Jacqmin Gadda, Hélène ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ DE BORDEAUX   (France)

^c UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Brier score; joint model; longitudinal data; mixture model; predictive accuracy; prognosis; prostate cancer

Indexed keywords

CANCER GROWTH; CANCER RADIOTHERAPY; CANCER RECURRENCE; CHI SQUARE TEST; CLASSIFICATION; CONTROLLED STUDY; FOLLOW UP; JOINT LATENT CLASS MODEL; LONGITUDINAL MARKER; LONGITUDINAL STUDY; MAXIMUM LIKELIHOOD METHOD; MEASUREMENT ACCURACY; PREDICTION; PROPORTIONAL HAZARDS MODEL; PROSTATE CANCER; REVIEW; SHARED RANDOM EFFECT MODEL; STATISTICAL MODEL; TIME TO EVENT;

BRIER SCORE; JOINT MODEL; LONGITUDINAL DATA; MIXTURE MODEL; PREDICTIVE ACCURACY; PROGNOSIS; PROSTATE CANCER;

COHORT STUDIES; HUMANS; LIKELIHOOD FUNCTIONS; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; PREDICTIVE VALUE OF TESTS; PROBABILITY; PROSTATE-SPECIFIC ANTIGEN; PROSTATIC NEOPLASMS; RECURRENCE; TUMOR MARKERS, BIOLOGICAL;

EID: 84892600752     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212445839     Document Type: Review

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