Effect size is the amount of difference between 2 groups. Often, statistical significance is reported, and the authors make a conclusion on whether there is a statistical difference that exists between 2 groups. Less often, the size of the difference between groups is reported. Effect size can provide valuable information to the clinician because it provides commentary as to what degree a treatment, for example, is effective. It is a scale-free measure; therefore, it can interpret the size of the difference between groups no matter which units are being measured.
The problem with using statistical significance as a measure of effectiveness is that a p-value depends on the size of the effect and the size of the sample. If the effect was large but the sample was small, one would get a significant result. Or, if the effect was small, but the sample was large, one might calculate statistical significance. Therefore, differences between groups may be a function of the sample size, rather than the true effectiveness of the intervention. Often, the effect size is reported using a 95% CI, which is the equivalent of significance at p< .05. The benefit of effect size is that studies can be compared retrospectively using a meta-analysis, and the effect sizes can be compared and averaged.10 Effect size provides a common comparison, even when studies have varying samples and designs.
Number Needed to Treat
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The NNT provides the clinician with the number of patients he or she would need to treat with the intervention to prevent one event. More practically, it represents the proportion of patients likely to have treatment-specific effects. It is used as a measure of the effectiveness of an intervention and goes beyond statistical significance to assist the clinician in practice decisions. Similar to effect size, it is easy to compare between interventions. The NNT is a treatment-specific measure, comparing the treatment group to a control group in gaining a certain clinical outcome. To calculate and apply NNTs to clinical practice, clear clinical outcome measures must be used. NNTs established from systematic reviews of RCTs provide the highest level of evidence; therefore, an effort should be made to use NNTs that have been established by sound scientific techniques. Specific criteria of quality, validity, and size must be met before analyzing the NNT.
The best NNT is equal to 1. This would mean that everyone who experienced the treatment benefited as compared to the control group in which no one experienced an improved outcome. The worst NNT is -1, meaning that no one improved with the treatment and everyone improved on their clinical outcome in the control group. A well-defined NNT should include specifics about the comparator, the clinical outcome, the duration of treatment, and the 95% CI. The NNT can be calculated using the following formula:
An example of an NNT statistic that is valuable to athletic trainers can be found in report by Sugimoto et al.11 The authors determined that when implementing primary ACL injury prevention programs, the NNT was 120 to prevent 1 ACL injury. This may present a dilemma when trying to gain support of implementation of such a program. Because so much time and effort is required to produce one positive outcome, this NNT does not support the value of such conditioning programs. More researchers need to corroborate this finding, and improvements to training programs may decrease the NNT.
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