How large was the treatment effect? What was the absolute risk reduction?
Results: At 2 years, diabetes remission had occurred in none of the patients receiving medical therapy, as compared with 15 of 20 (75%) undergoing gastric bypass and 19 of 20 (95%) undergoing biliopancreatic diversion (P<0.001 for both comparisons). There was a significant association between study group and rate of remission. However, since there were no remissions in the medical-therapy group, risk ratios were computed in a more conservative fashion on the assumption that remission had occurred in the 2 patients in the medical-therapy group who dropped out.
|Remission of Diabetes||No remission of Diabetes|
Experimental Event Rate (EER) = 15 / 20 = 75%
outcome present / total in experimental group
Control Event Rate (CER) = 2 / 20 = 10%
outcome present / total in control group
Absolute Benefit Increase (ABI) = 75% - 10% = 65%
is the arithmetic difference between the rates of events in the experimental and control group. An Absolute Benefit Increase (ABI) refers to the increase of a good event as a result of the intervention. An Absolute Risk Reduction (ARR) refers to the decrease of a bed event as the result of the intervention. [ARR = EER-CER]
Relative Risk (RR) = .75 / .10 = 7.5
is the ratio of the risk in the experimental group compared to the risk in the control group.proportional reduction in risk between the rates of events in the control group and the experimental group. [RR = EER/CER]
Relative Benefit Increase (RBI) = 65% / 10% = 650%
is the proportional increase in benefit between the rates of events in the control group and the experimental group. [RBI = EER - CER / CER]
Numbers Needed to Treat (NNT) = 1 / .65 = 2
is the number of patients who need to be treated to prevent one bad outcome or produce one good outcome. In other words, it is the number of patients that a clinician would have to treat with the experimental treatment compared to the control treatment to achieve one additional patient with a favorable outcome. [NNT = 1/ARR]
Clinical versus Statistical Significance
"Although it is tempting to equate statistical significance with clinical importance, critical readers should avoid this temptation. To be clinically important requires a substantial change in an outcome that matters. Statistically significant changes, however, can be observed with trivial outcomes. And because statistical significance is powerfully influenced by the number of observations, statistically significant changes can be observed with trivial (small) changes in important outcomes. Large studies can be significant without being clinically important and small studies may be important without being significant." (Effective Clinical Practice, July/August 2001, ACP)
Clinical significance has little to do with statistics and is a matter of judgment. Clinical significance often depends on the magnitude of the effect being studied. It answers the question "Is the difference between groups large enough to be worth achieving?" Studies can be statistically significant yet clinically insignificant.
For example, a large study might find that a new antihypertensive drug lowered BP, on average, 1 mm Hg more than conventional treatments. The results were statistically significant with a P Value of less than .05 because the study was large enough to detect a very small difference. However, most clinicians would not find the 1 mm Hg difference in blood pressure large enough to justify changing to a new drug. This would be a case where the results were statistically significant (p value less than .05) but clinically insignificant.
Guyatt, G. Rennie, D. Meade, MO, Cook, DJ. Users' Guide to Medical Literature: A Manual for Evidence-Based Clinical Practice, 2nd Edition 2008.