The sample size for a clinical trial: a Bayesian-decision theoretic approach.
Using decision theory, what is an appropriate sample size for a clinical trial, with a binary endpoint? We present a program, suitable for actual planning, which, with some extensions, implements Canner's solution to this question. Examples with a discussion are given. Implications of a Bayesian approach are discussed. Bayesian and Neyman--Pearson approaches are compared.
Stanford University School of Medicine, Department of Health Research and Policy, HRP Redwood Building, Stanford, California 94305-5405, USA. funn@stanford.edu
J
J
Halpern
B W
BW
Brown
J
J
Hornberger
D001499:Bayes Theorem; D003131:Combined Modality Therapy; D005492:Folic Acid / Q000627:therapeutic use; D006801:Humans; D007674:Kidney Diseases / Q000188:drug therapy / Q000628:therapy; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D006435:Renal Dialysis; D018401:Sample Size
D005492:Folic Acid