Is It Time for Going Beyond the P-Value Paradigm With the Estimation of the Probability of Clinical Benefit as a Criterion for Assessing the Outcomes of a Clinical Trial? A Case Study in Patients With Major Depressive Disorder

The conventional statistical methodologies for evaluating treatment effect are based on hypothesis testing (P-value). Alternative measurements of treatment effect have been proposed for anti-infective treatments using the probability of target attainment. A general framework is proposed to extend this methodology to other therapeutic areas. A disease trial model is used for estimating the probability of reaching a treatment effect associated with relevant clinical benefits, in complement to the evaluation of the probability of rejecting the null hypothesis. A case study is presented in depression, where disease status is evaluated using bounded clinical scores (Hamilton Depression Rating Scale), and detectable treatment effect is inversely proportional to placebo response. The beta-regression approach is used to model Hamilton scale scores, and a placebo-related criterion is proposed for determining the clinical benefit. The probability of reaching a clinical benefit represents a reliable criterion for replacing the P-value paradigm in the assessment of the outcomes of clinical trials.