A likelihood-based approach to P-value interpretation provided a novel, plausible, and clinically useful research study metric

Interpretation of clinical research findings using the paradigm of null hypothesis significance testing has a number of limitations. These include arbitrary dichotomization of results, lack of incorporation of study power and prior probability, and the confusing use of conditional probability. This study aimed to describe a novel method of P-value interpretation that would address these limitations. Published clinical research was reinterpreted using the delta likelihood ratio. The delta likelihood ratio is an application of Bayes’ rule incorporating the P-value and study power. Calculation of the delta likelihood ratio allows the determination of the most likely effect size using the maximum likelihood principle. We showed that the delta likelihood is easily calculated and produces plausible results using the example of several previously published research studies. Empirical evidence of validity was demonstrated by simulation. The delta likelihood ratio and most likely effect size are simple and intuitive metrics to summarize research findings. The delta likelihood ratio incorporates study power and provides a continuous measure of the probability that the research result is a true effect. The most likely effect size is an easily understood metric that should aid the interpretation of research.