Multiplicity-Adjusted Sample Size Requirements: A Strategy to Maintain Statistical Power With
J Clin Psychiatry 2004;65:1511-1514
© Copyright 2014 Physicians Postgraduate Press, Inc.
Purchase This PDF for $40.00
If you are not a paid subscriber, you may purchase the PDF.
(You'll need the free Adobe Acrobat Reader.)
Receive immediate full-text access to JCP. You can subscribe to JCP online-only ($86) or print + online ($156 individual).
With your subscription, receive a free PDF collection of the NCDEU Festschrift articles. Hurry! This offer ends December 31, 2011.
If you are a paid subscriber to JCP and do not yet have a username and password, activate your subscription now.
As a paid subscriber who has activated your subscription, you have access to the HTML and PDF versions of this item.
Click here to login.
Did you forget your password?
Still can't log in? Contact the Circulation Department at 1-800-489-1001 x4 or send email
Background: A researcher must carefully balance the risk of 2 undesirable outcomes when designing a clinical trial: false-positive results (type I error) and false-negative results (type II error). In planning the study, careful attention is routinely paid to statistical power (i.e., the complement of type II error) and corresponding sample size requirements. However, Bonferroni-type alpha adjustments to protect against type I error for multiple tests are often resisted. Here, a simple strategy is described that adjusts alpha for multiple primary efficacy measures, yet maintains statistical power for each test.
Method: To illustrate the approach, multiplicity-adjusted sample size requirements were estimated for effects of various magnitude with statistical power analyses for 2-tailed comparisons of 2 groups using chi2 tests and t tests. These analyses estimated the required sample size for hypothetical clinical trial protocols in which the prespecified number of primary efficacy measures ranged from 1 to 5. Corresponding Bonferroni-adjusted alpha levels were used for these calculations.
Results: Relative to that required for 1 test, the sample size increased by about 20% for 2 dependent variables and 30% for 3 dependent variables.
Conclusion: The strategy described adjusts alpha for multiple primary efficacy measures and, in turn, modifies the sample size to maintain statistical power. Although the strategy is not novel, it is typically overlooked in psychopharmacology trials. The number of primary efficacy measures must be prespecified and carefully limited when a clinical trial protocol is prepared. If multiple tests are designated in the protocol, the alpha-level adjustment should be anticipated and incorporated in sample size calculations.