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This is an updated version of my Common Measures of Association presentation. I've updated it to include (1) more detail on rates, risks, and proportions, (2) Absolute Risk Reduction (ARR), Attributable Risk (AR), Number Needed to Treat (NNT) and Number Needed to Harm (NNH). Feel free to email me for a full version of the slideshow.
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C onsider the following hypothet- ical scenario and results that are formatted in evidence-based practice (EBP) language such as those that you might see in the Cochrane Collaboration,1 a primary resource for evidence-based systematic reviews. Scenario: How effective is a daily dose of 500 mg of vitamin C in the prevention of ulcers on the heels of bedridden elderly clients? Results: With an NNT of 5, vitamin C is effective (OR, 0.10; 95% CI, 0.05-0.20). If there are some abbreviations or values in this situation that are un- familiar to you, you will find explana- tions and examples in this article that will help you in reading, interpreting, and understanding them as you use evidence-based literature for your best practices. Nurses always rise to the occasion to learn the latest research information that may improve patient care and outcomes. An obstacle to involvement in EBP is lack of skill in understanding the ‘‘bottom line’’ of systematic reviews and accompanying risk-related num- bers. 2-4 Content and research experts conduct systematic reviews using strict criteria for inclusion of primary re- search studies and statistical analysis. 5 The Cochrane Collaboration is a major resource for more than 1,000 system- atic reviews of randomized clinical trials for the effects of healthcare inter- ventions created through collaboration of more than 50 worldwide review and methods teams.6 The systematic review teams basi- cally seek the response to 1 question: how many people have a bad outcome in the experimental group compared with the control group? Bad outcomes refer to the undesirable outcomes in a study, such as development of a heel ulcer. Noteworthy in EBP statistics is the simplicity of using a head count rather than group averages. Even when individual study results are not statisti- cally significant, if the experimental group has fewer bad outcomes than the control group, the nurse or other pro- vider might want to apply the results anyway. Seven terms and their abbre- viations and formulas are common in the reported results,7,8 as summarized in Table 1. In this article, hypothetical examples and their derivations de- scribe these 7 terms. At the end, you can derive these values for a clinical scenario toward a better understanding when teaching these terms to others. Absolute Risk Reduction Absolute risk reduction (ARR) is the absolute arithmetic difference (abso- lute means that one ignores plus and minus signs) in percentages of bad outcomes between the experimental and control groups. Absolute risk reduc- tion means that more people in the control group than in the experimen- tal group develop a bad outcome. To calculate the ARR, you need to know just 2 things: the experimental event rate (EER), or the percentage of the bad outcome in the experimental group; and the control group event rate (CER), or the percentage of the bad outcome in the control group. Let us look at an ex- ample: 13% of patients with diabetes receiv.
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