Sufficient trial size to inform clinical practice

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 113; no. 38; pp. 10518 - 10523
• Main Authors: Manski, Charles F., Tetenov, Aleksey
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 20.09.2016
• Subjects: Bayes Theorem; Biological Sciences; Biomedical Research - statistics & numerical data; Clinical medicine; Decision theory; Expected utility; Humans; Hypothesis testing; Medical research; Models, Statistical; Randomized Controlled Trials as Topic - statistics & numerical data; Sample Size; Social Sciences; medical decision making; clinical trials; near optimality; sample size
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1612174113

Medical research has evolved conventions for choosing sample size in randomized clinical trials that rest on the theory of hypothesis testing. Bayesian statisticians have argued that trials should be designed to maximize subjective expected utility in settings of clinical interest. This perspective is compelling given a credible prior distribution on treatment response, but there is rarely consensus on what the subjective prior beliefs should be. We use Wald’s frequentist statistical decision theory to study design of trials under ambiguity. We show that ε-optimal rules exist when trials have large enough sample size. An ε-optimal rule has expected welfare within ε of the welfare of the best treatment in every state of nature. Equivalently, it has maximum regret no larger than ε. We consider trials that draw predetermined numbers of subjects at random within groups stratified by covariates and treatments. We report exact results for the special case of two treatments and binary outcomes. We give simple sufficient conditions on sample sizes that ensure existence of ε-optimal treatment rules when there are multiple treatments and outcomes are bounded. These conditions are obtained by application of Hoeffding large deviations inequalities to evaluate the performance of empirical success rules.