P-values, Power, and Efficacy

Lemuel A. Moyé ()
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Lemuel A. Moyé: University of Texas, Health Science Center at Houston, School of Public Health

Chapter 6 in Statistical Reasoning in Medicine, 2006, pp 137-156 from Springer

Abstract: 6.13 Conclusions P-values, power, and the effect size of the research effort are all intertwined, and this complicated interrelationship can lead to danger for the unwary research designer. Forcing type I error to extremely low levels, ceteris paribus will suck the power from the experiment. Sample size computations in Appendix B demonstrate how to incorporate type I and type II error concerns into an experiment. The investigations should first identify the efficacy level of clinical interest and then choose the sample size that allows this efficacy level to be identified with statistical significance. However, sample size computations that are only composed of mathematics are subject to the strong persistent undertow exerted by the resource centers of a clinical research effort. This is appropriate, and dialogue between all involved parties should be frank and open, for the good of the research. Once the experiment is underway, discordant concerns preclude major changes in sample size goals. Therefore, honest discussion of logistical and financial issues should be encouraged during the research program’s design phase.

Keywords: Myocardial Infarction; Event Rate; Percent Reduction; Sample Size Computation; Total Mortality (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1007/978-0-387-46212-7_7

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