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

J Clin Pharmacol. 2018 Jun;58(6):740-749. doi: 10.1002/jcph.1074. Epub 2018 Jan 25.


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 β-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.

Keywords: P-value; clinical benefit; placebo response; treatment effect; β-regression.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clinical Trials as Topic / methods*
  • Clinical Trials as Topic / statistics & numerical data
  • Depressive Disorder, Major / psychology*
  • Humans
  • Monte Carlo Method
  • Outcome Assessment, Health Care / methods*
  • Outcome Assessment, Health Care / statistics & numerical data
  • Placebo Effect
  • Probability