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How do we assess the Clinical Relevance of a Scientific Finding? Is there a Clinical Relevance Index?

Most clinicians would be familiar with the assessment of statistical significance through the use of P values. They would also accept that statistical significance is not the same as clinical relevance. Clinical relevance may be more important, but often appears subjective. Is there a more objective measure using information already provided in most clinical studies? We know that the clinical relevance of any intervention is directly related to the observed effect size and its confidence limits. What we need to know, in addition, is how this relates to the effect size of interest (clinically worthwhile difference, minimum important difference). As it happens, the effect size of interest is a mandatory variable in any a priori calculation of statistical power, along with the expected sample standard deviation. The effect size of interest is the minimum effect size the authors consider clinically relevant. Therefore, dividing the observed effect size by the effect size of interest should provide an index of clinical relevance. It may be argued that the effect size of interest is not always a clinically relevant value, because it may be influenced by pragmatic considerations limiting the sample size. However, this would then expose the pragmatic nature of the ‘effect size of interest’ chosen by the authors. It would be inappropriate for authors to argue post hoc, that their own choice of ‘effect size of interest’ was too large. For a positive finding, a clinical relevance index of <1 would indicate that the observed effect is clinically irrelevant, despite a ‘significant’ P value. It is not possible to escape this conclusion. Anything less than the minimum effect size of interest must be of no interest. On the other hand, if the observed effect size is greater than the effect size of interest, the index will be >1 and would therefore be clinically relevant. Other aspects of the observed effect could then be assessed, such as its precision and external validity. For a negative finding, the effect size of interest remains equally important. Acceptance of a null hypothesis does not mean that there is definitely no difference. It means that the difference is most likely less than the effect size of interest in the power calculation. The effect size of interest represents the maximum effect that can be excluded, with a likelihood equal to the statistical power (e.g. a power of 0.8, if appropriately calculated, indicates that this likelihood is 80%). While it may be inappropriate for authors to challenge their own effect size of interest, there is no such limitation on readers. Readers might find that the effect size of interest chosen by the authors does not exclude a clinically relevant difference. If so, the finding is inconclusive. It follows that if the effect size of interest is not reported, the clinical relevance of a statistical finding cannot be assessed fully. This applies to all baseline comparisons and secondary, subgroup or exploratory outcomes that do not have an a priori power calculation. While the effect size of interest or power can be determined post hoc, this is not a valid approach to hypothesis testing. Multivariate analysis is another statistical technique that is rarely accompanied by a power calculation. The ratio of ‘observed effect size’ to ‘effect size of interest’ as a clinical relevance index may not have been described commonly in statistical texts. Nevertheless, it is implicit in inferential statistical methodology. Readers of clinical papers need not be intimidated by statistics. To assess the clinical relevance of a positive finding, they should divide the observed effect size by the effect size of interest in the power calculation. Clinically relevant findings will have a ratio ≥1. For negative findings, they should scrutinise the effect size of interest in the power calculation. If the effect size of interest does not exclude a clinically relevant effect, the finding is inconclusive. If this basic information is not provided, clinical relevance or lack of clinical relevance cannot be fully assessed.