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Spring 2005
STATISTICS COLLOQUIUM
Friday, January 14, 2005
2:30-3:00—Refreshments
3:00-4:00—Talk
Yost Hall, Room 300
Ralph O'Brien
Cleveland Clinic Foundation
Sample-Size Analysis in Study Planning: Concepts and Issues Ralph O'Brien, PhD Cleveland Clinic Foundation Prospective sample-size analysis is invaluable to research design, promoting wiser allocation of scientific resources and stronger bioethics. Moreover, the process itself induces excellence and breadth in scientific planning by requiring the research team to delineate, critique, and tighten the research questions, study rationale, and many aspects of study design, including outcome measurements and analysis plans. Critically, the team must make reasonable conjectures about the “infinite datasets” representing the study populations. This is supported by ever-improving methods and software for computing power and/or required sample sizes under multiple plausible scenarios. In contrast, ritualistic sample-size and power computations, promulgated with little consideration of scientific context, are an empty exercise at best.
This tutorial will use PROC POWER (new in SAS 9) to handle the statistical planning for a realistic study. This involves, in part, (1) positioning the study in a line of scientific investigation (early to middle to late in “The March of Science”); (2) sizing a study for precision of an statistical interval or power of a conventional hypothesis test; (3) considering positive and negative inference mistake rates (false discovery and false miss rates) rather than traditional Type I and II error rates; and (4) communicating power and sample size concepts and results to non-statistician investigators.
Non-SAS users should have no difficulty applying these notions to other software systems. Although the methods covered here will be frequentist, basic Bayesian concepts help to clarify and shape the planning process for both investigators and statisticians.
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