Study size has typically been planned based on statistical power and therefore has been heavily influenced by the philosophy of statistical hypothesis testing. A worthwhile alternative is to plan study size based on precision, for example by aiming to obtain a desired width of a confidence interval for the targeted effect. This article presents formulas for planning the size of an epidemiologic study based on the desired precision of the basic epidemiologic effect measures.
From the aResearch Triangle Institute, Research Triangle Park, NC
bBoston University School of Public Health, Boston, MA
cDepartment of Epidemiology and Department of Statistics, University of California, Los Angeles, CA.
Submitted December 26, 2017; accepted May 30, 2018.
The authors report no conflicts of interest.
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
Correspondence: Kenneth J. Rothman, Epidemiology, RTI Health Solutions, 200 Park Offices Drive, Research Triangle Park, NC 27709. E-mail: KRothman@rti.org.