COUNTERPOINT: Statistical analysis in NIH peer review—identifying innovation Thoru Pederson 1 University of Massachusetts Medical School, Worcester, Massachusetts, USA 1 Correspondence: University of Massachusetts Medical School, 377 Plantation St., Worcester, MA 01605, USA. E-mail: thoru.pederson@umassmed.edu DAVID KAPLAN ’ S "Statistical analysis in NIH peer review—identifying innovation," proposes that NIH’s Center for Scientific Review adopt the use of certain statistical concepts in evaluating grant applications. The author’s key point is his belief that this practice would enhance the detection of innovative proposals. The hypothesis that the present review system is not ideal for detecting (or approving) innovative proposals is actually not easy to document (nor does the author attempt to do so). Nonetheless, his proposal can be evaluated on its intrinsic attributes. Although he does not specify this, it is understood that the type of applications being discussed are investigator-initiated proposals. Applications requested by NIH through RFPs are by definition narrow in research focus and therefore have a topically more homogenous panel of reviewers. Large program project grant applications, including ones with investigators at more than one institution, also have features of review that differ from standard R01-type applications. Let us assume the author is mainly discussing the latter.
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COUNTERPOINT: Statistical analysis in NIH peer reviewidentifying innovation
Pederson, Thoru
Follow The FASEB Journal
, Volume 21 (2): 309
Fed of American Socs for Experimental Biology – Feb 1, 2007
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