A Bayesian approach to jointly modeling toxicity and biomarker expression in a phase I/II dose-finding trial

Biometrics

Volumn 61, Issue 2, 2005, Pages 343-354

  Bekele, B Nebiyou ^a   Shen, Yu ^a  

^a UNIVERSITY OF TEXAS   (United States)

Author keywords

Adaptive design; Bayesian inference; Correlated binary continuous variables; Latent variables; Markov chain Monte Carlo

Indexed keywords

BAYESIAN NETWORKS; BIOACTIVITY; DISEASES; GENE EXPRESSION; INFERENCE ENGINES; MARKOV PROCESSES; MEDICAL APPLICATIONS; PATIENT TREATMENT; STATE SPACE METHODS;

ADAPTIVE DESIGNS; BAYESIAN APPROACHES; BAYESIAN INFERENCE; CONTINUOUS VARIABLES; CORRELATED BINARY-CONTINUOUS VARIABLE; DOSE FINDING; LATENT VARIABLE; MARKOV CHAIN MONTE CARLO; MARKOV CHAIN MONTE-CARLO; PHASE I;

TOXICITY;

ADENOVIRUS VECTOR; BIOLOGICAL MARKER; NEW DRUG; PROTEIN; SYN3; UNCLASSIFIED DRUG;

BAYESIAN ANALYSIS; DOSE-RESPONSE RELATIONSHIP; TOXICITY;

ARTICLE; BAYES THEOREM; BLADDER CANCER; CLINICAL ARTICLE; CLINICAL TRIAL; DOSE CALCULATION; GENE THERAPY; GENETIC TRANSDUCTION; HUMAN; MAXIMUM TOLERATED DOSE; MONTE CARLO METHOD; ONCOLOGY; PHASE 1 CLINICAL TRIAL; PHASE 2 CLINICAL TRIAL; PROBABILITY; PROTEIN SECRETION; PROTEIN URINE LEVEL; STATISTICAL MODEL; TREATMENT OUTCOME;

ADENOVIRIDAE;

EID: 20744435134     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2005.00314.x     Document Type: Article

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