The use of bayesian hierarchical models for adaptive randomization in biomarker-driven phase II studies

Journal of Biopharmaceutical Statistics

Volumn 25, Issue 1, 2015, Pages 66-88

  Barry, William T ^a   Perou, Charles M ^b   Marcom, P Kelly ^c   Carey, Lisa A ^b   Ibrahim, Joseph G ^d  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c DUKE UNIVERSITY MEDICAL CENTER   (United States)

^d University of North Carolina at Chapel Hill   (United States)

Author keywords

Integral biomarkers; Phase II trials; Response adaptive

Indexed keywords

BIOLOGICAL MARKER; TUMOR MARKER;

ADAPTIVE RANDOMIZATION; ARTICLE; BAYES THEOREM; HUMAN; MAXIMUM LIKELIHOOD METHOD; MOTIVATION; PREVALENCE; PRIORITY JOURNAL; RANDOMIZATION; RISK FACTOR; SENSITIVITY AND SPECIFICITY; SIMULATION; COMPUTER SIMULATION; METABOLISM; NEOPLASMS; PHASE 2 CLINICAL TRIAL (TOPIC); PREDICTIVE VALUE; RANDOMIZED CONTROLLED TRIAL (TOPIC); SAMPLE SIZE; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TREATMENT OUTCOME;

BAYES THEOREM; BIOMARKERS, TUMOR; CLINICAL TRIALS, PHASE II AS TOPIC; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; NEOPLASMS; PREDICTIVE VALUE OF TESTS; RANDOM ALLOCATION; RANDOMIZED CONTROLLED TRIALS AS TOPIC; SAMPLE SIZE; TREATMENT OUTCOME;

EID: 84921447068     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2014.919933     Document Type: Article

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