A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer

Statistics in Medicine

Volumn 27, Issue 9, 2008, Pages 1468-1489

  Zhou, Huafeng ^a   Lawson, Andrew B ^a   Hebert, James R ^a,b   Slate, Elizabeth H ^b   Hill, Elizabeth G ^b  

^a UNIVERSITY OF SOUTH CAROLINA   (United States)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

Bayesian hierarchical model; Deviance information criterion (DIC); Ordinal categorical data; Prostate cancer; Spatial distribution pattern

Indexed keywords

PROSTATE SPECIFIC ANTIGEN;

ACCURACY; ADULT; AGED; ARTICLE; BAYES THEOREM; CANCER INCIDENCE; CANCER REGISTRY; CANCER SCREENING; CANCER STAGING; CANCER SURVIVAL; CONTROLLED STUDY; EARLY DIAGNOSIS; GEOGRAPHIC DISTRIBUTION; GEOGRAPHIC INFORMATION SYSTEM; HUMAN; MAJOR CLINICAL STUDY; MALE; PROBABILITY; PROSTATE CANCER; RACE DIFFERENCE; RISK FACTOR; SENSITIVITY ANALYSIS; STATISTICAL MODEL; SURVIVAL RATE; UNITED STATES;

BAYES THEOREM; BIOMETRY; HUMANS; LOGISTIC MODELS; MALE; MODELS, STATISTICAL; NEOPLASM STAGING; PROSTATIC NEOPLASMS; SOUTH CAROLINA;

EID: 42149162271     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3024     Document Type: Article

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