Making Statistics Clinically Meaningful

The growth in evidence-based medicine clearly benefits patient care but it is important that evidence is accessible and usable. Translating research results into usable, meaningful information can be challenging and impede the implementation of robust evidence. There are many aspects to making the statistical parts of a research study actionable for practicing clinicians and policy makers. These include ensuring that the right questions are asked, the study is designed appropriately, and results are transparent and applicable to the clinical setting. In this perspectives-style article we offer guidance on these considerations by highlighting several approaches that we have found effective for improving the interpretability and practical application of statistical findings. Our over-arching aim is to stimulate interdisciplinary dialogue throughout the research process. Specifically, we discuss the interpretation of p-values, effect estimates, differences between means, scaling regression coefficients, unadjusted/adjusted estimates, Minimal Clinically Important Difference, absolute and relative risk, and suggest how clinical meaning can be enhanced by presenting the same information in different but complementary ways. We conclude with a recommendation that study teams prioritize interdisciplinary discussions around clinical meaningfulness throughout our research studies to maximize their clinical impact.