Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcomes

Statistics in Medicine

Volumn 21, Issue 24, 2002, Pages 3897-3911

  Pauler, Donna K ^a   Finkelstein, Dianne M ^b  

^a Fred Hutchinson Cancer Research Center   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

Bayesian modelling; Markov chain Monte Carlo; PSA screening; Recurrence

Indexed keywords

BIOLOGICAL MARKER; PROSTATE SPECIFIC ANTIGEN;

ARTICLE; BAYES THEOREM; BLOOD EXAMINATION; CANCER DIAGNOSIS; CANCER GROWTH; CANCER PATIENT; CANCER RADIOTHERAPY; CANCER RECURRENCE; CANCER REGRESSION; CANCER SCREENING; CANCER SURGERY; CLINICAL PRACTICE; DATA ANALYSIS; DISEASE COURSE; EARLY DIAGNOSIS; FOLLOW UP; GENERAL HOSPITAL; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL DECISION MAKING; METASTASIS; MONTE CARLO METHOD; NONBIOLOGICAL MODEL; NONLINEAR SYSTEM; OUTCOMES RESEARCH; PHYSICIAN; PREDICTION; PROBABILITY; PROSTATE CANCER; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; TIME; UNITED STATES;

BAYES THEOREM; DISEASE PROGRESSION; FOLLOW-UP STUDIES; HUMANS; MALE; MARKOV CHAINS; MASSACHUSETTS; MODELS, BIOLOGICAL; MONTE CARLO METHOD; NEOPLASM RECURRENCE, LOCAL; PREDICTIVE VALUE OF TESTS; PROPORTIONAL HAZARDS MODELS; PROSTATE-SPECIFIC ANTIGEN; PROSTATIC NEOPLASMS;

EID: 0037203110     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1392     Document Type: Article

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