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The Use of Predicted Confidence Intervals When Planning Experiments and the Misuse of Power When Interpreting Results

Steven N. Goodman, MD, PhD; and Jesse A. Berlin, ScD
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From Johns Hopkins University School of Medicine, Baltimore, Maryland; the University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania. Requests for Reprints: Steven Goodman, MD, PhD, Oncology Center, Division of Biostatistics, Johns Hopkins University, 550 North Broadway, Suite 1103, Baltimore, MD 21205.

Copyright ©2004 by the American College of Physicians

Ann Intern Med. 1994;121(3):200-206. doi:10.7326/0003-4819-121-3-199408010-00008
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Although there is a growing understanding of the importance of statistical power considerations when designing studies and of the value of confidence intervals when interpreting data, confusion exists about the reverse arrangement:the role of confidence intervals in study design and of power in interpretation. Confidence intervals should play an important role when setting sample size, and power should play no role once the data have been collected, but exactly the opposite procedure is widely practiced. In this commentary, we present the reasons why the calculation of power after a study is over is inappropriate and how confidence intervals can be used during both study design and study interpretation.


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Figure 1.
Predicted 95% confidence intervals. Top.Bottom.

Predicted 95% confidence intervals for hypothetical findings of 0%, 15%, and 25% differences when the sample size is set with power of 90% for an absolute difference of 25% between two treatments (see text). Predicted 95% confidence intervals for findings of 0%, 15% and 25% differences when the sample size is set with power of 90% for an absolute difference of 10% between two treatments (see text).

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