Research Pearls
Editorial Commentary: The Power of Interpretation: Utilizing the P Value as a Spectrum, in Addition to Effect Size, Will Lead to Accurate Presentation of Results
Statistics have helped develop evidence-based medicine. Comparing groups and rejecting (or not) a null hypothesis is a main principle of the scientific method. Many studies have demonstrated that drawing conclusions based on the statistical result of a dichotomic P value instead of a spectrum can mislead us to conclude that there is “no difference” between two groups, or two treatments. In addition to the P value, the utilization of effect size (magnitude of difference between studied groups), may help us obtain a better global understanding of the statement “no effect”.Authors Dichotomize Medical Research Findings as Significant Versus Not Significant, Creating a False Sense of Certainty, and Report Outcomes on Patients Whose Results Have Been Previously Reported Without Proper Disclosure
Statistical significance dichotomizes research findings into significant versus not significant, creating a false sense of certainty. It is insufficient to mindlessly report results as significant versus not significant without providing a quantitative estimate of the uncertainty of the data. Authors could provide a confidence interval, draw a P value function graph, or run a Bayesian analysis. Authors could calculate and report a Surprise or S value. Most importantly, authors could thoughtfully consider how the uncertainty within their research data informs the results of their study.‘No Effect’ Conclusions in Studies Reporting Nonsignificant Results Are Potentially Incorrect
To examine the spectrum of effect sizes in line with “no effect” claims in clinical studies published in high-impact orthopaedic journals.Understanding Network Meta-analysis (NMA) Conclusions Requires Scrutiny of Methods and Results: Introduction to NMA and the Geometry of Evidence
Synthesis of medical literature to determine the best treatment for a given problem is challenging, particularly when multiple options exist. Network meta-analysis (NMA) allows the comparison of different treatment approaches in a single, systematic review including treatments that have never been compared head-to-head. A key to understanding NMA is to focus on the network geometry showing the number of included studies and their relationships: different treatment options are illustrated as nodes.Nearly One-Third of Published Systematic Reviews and Meta-analyses Yield Inconclusive Conclusions: A Systematic Review
To perform a systematic review that determines the percentage of published orthopedic surgery and sports medicine systematic reviews and meta-analyses that have a conclusive conclusion.Misinterpretation of P Values and Statistical Power Creates a False Sense of Certainty: Statistical Significance, Lack of Significance, and the Uncertainty Challenge
Despite great advances in our understanding of statistics, a focus on statistical significance and P values, or lack of significance and power, persists. Unfortunately, this dichotomizes research findings comparing differences between groups or treatments as either significant or not significant. This creates a false and incorrect sense of certainty. Statistics provide us a measure of the degree of uncertainty or random error in our data. To improve the way in which we communicate and understand our results, we must include in reporting a probability, or estimate, of our degree of certainty (or uncertainty).
