A Bayesian approach estimating treatment effects on biomarkers containing zeros with detection limits

Statistics in Medicine

Volumn 27, Issue 13, 2008, Pages 2497-2508

  Chu, Haitao ^a   Nie, Lei ^b   Kensler, Thomas W ^c  

^a IL   (United States)

^b GEORGETOWN UNIVERSITY   (United States)

^c JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Bayesian methods; Clinical trials; Left censoring; Mixture models; Model selection

Indexed keywords

AFLATOXIN B1; AFLATOXIN M1; OLTIPRAZ; PLACEBO; TUMOR MARKER;

ARTICLE; BAYES THEOREM; CANCER DIAGNOSIS; CANCER PREVENTION; CHINA; CLINICAL TRIAL; DRUG MEGADOSE; HUMAN; LIVER CELL CARCINOMA; MAXIMUM LIKELIHOOD METHOD; MONTE CARLO METHOD; PROBABILITY; URINALYSIS;

AFLATOXIN M1; BAYES THEOREM; BIOLOGICAL MARKERS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; MARKOV CHAINS; MONTE CARLO METHOD; PYRAZINES;

EID: 44949176565     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3170     Document Type: Article

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