Using prior information to allocate significance levels for multiple endpoints.
We maximize power in a replicated clinical trial involving multiple endpoints by adjusting the individual significance levels for each hypothesis, using preliminary data to obtain the optimal adjustments. The levels are constrained to control the familywise error rate. Power is defined as the expected number of significances, where expectations are taken with respect to the posterior distributions of the non-centrality parameters under non-informative priors. Sample size requirements for the replicate study are given. Intuitive principles such as downweighting insignificant variables from a preliminary study and giving primary endpoints more emphasis are justifiable within the conceptual framework.
Westfall
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PH
Department of Information Systems and Quantitative Sciences, Texas Tech University, Lubbock 79409, USA. westfall@ttu.edu
Krishen
A
A
Young
S S
SS
D000704:Analysis of Variance; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011336:Probability; D018401:Sample Size