False negatives in evidence based medicine

Kault, David (2014) False negatives in evidence based medicine. Journal of Medical Statistics and Informatics, 2 (5).

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Abstract

Evidence Based Medicine (EBM) is a term used for the current dominant methodology for deciding what medical treatments should be accepted as valid. It places great emphasis on Randomised Clinical Trials (RCTs) which are analysed according to a strict frequentist paradigm, with a rigid p-value ≤0.05 criterion but with little consideration of prior probabilities or the cost of errors. Accordingly, low cost, safe treatments where there is prior knowledge of at least slight effectiveness, may often be inappropriately discarded by EBM. The Cochrane Collaboration is an online central repository of RCTs and meta-analyses of RCTs. This paper uses statistical methods applied to a random sample of outcomes listed in the Cochrane Collaboration, to estimate the negative predictive value when treatments are declared ineffective as a result of positive outcomes which do not achieve the p≤ 0.05 criterion. The data were analysed using six different models in order to determine the proportion of genuinely ineffective treatments in the set of all positive outcomes where p>0.05. All six methods give point estimates substantially less than half for the negative predictive value when the decision rule is to declare treatments to be ineffective when their outcome is positive but p>0.05. Although confidence interval estimation indicates considerable uncertainty in these estimates, it seems reasonable to conclude that when a RCT gives a positive outcome but p0.05, the conventional EBM decision to declare the treatment to be ineffective, is likely to be wrong more often than not.

Item ID: 33278
Item Type: Article (Research - C1)
ISSN: 2053-7662
Keywords: evidence based medicine; false negatives; false non-discovery rate; low cost treatments; negative predictive value; statistical models; type II error
Additional Information:

© 2014 David Kault ; licensee Herbert Publications Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Date Deposited: 28 Aug 2014 00:44
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50%
01 MATHEMATICAL SCIENCES > 0104 Statistics > 010499 Statistics not elsewhere classified @ 50%
SEO Codes: 92 HEALTH > 9202 Health and Support Services > 920204 Evaluation of Health Outcomes @ 100%
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