Key result
Simple methods for nonstatisticians to evaluate sample size calculations can identify serious errors, such as a five-fold underestimation in the HIP PRO trial, improving the peer review process.
Provides simple methods for nonstatisticians to verify sample size calculations in clinical trials, highlighting the importance of catching serious errors during peer review.
Simple methods let nonstatisticians catch sample size errors in review; extends accessible verification tools for trial reporting.
In this paper, we shall provide simple methods where nonstatisticians can evaluate sample size calculations for most large simple trials, as an important part of the peer review process, whether a grant, an Institutional Review Board review, an internal scientific review committee, or a journal referee. Through the methods of the paper, not only can readers determine if there is a major disparity, but they can readily determine the correct sample size. It will be of comfort to find in most cases that the sample size computation is correct, but the implications can be major for the minority where serious errors occur. We shall provide three real examples, one where the sample size need was seriously overestimated, one (HIP PRO-test of a device to prevent hip fractures) where the sample size need was dramatically underestimated, and one where the sample size was correct. The HIP PRO case is especially troubling as it went through an NIH study section and two peer reviewed journal reports without anyone catching this sample size error of a factor of more than five-fold.
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Jonathan J. Shuster (2013) conducted a review in Clinical Trials. Sample size verification methods was evaluated. Simple methods for nonstatisticians to evaluate sample size calculations can identify serious errors, such as a five-fold underestimation in the HIP PRO trial, improving the peer review process.
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