A Practical Guide to Interpret a Randomized Controlled Trial

TL;DR

This paper presents a practical, algorithm-based framework for interpreting randomized controlled trial results using confidence intervals and Bayesian analysis, aiming to avoid common misinterpretations.

Contribution

It introduces a novel classification system for RCT outcomes into six categories based on CI position and Bayesian probabilities, integrating multiple guidelines into one decision algorithm.

Findings

Same p > 0.05 result can mean different conclusions

Bayesian reanalysis can identify benefits missed by frequentist methods

Framework demonstrated with real-world clinical trial examples

Abstract

The most dangerous error in clinical trial interpretation is equating p > 0.05 with no effect. This review provides a practical, algorithm-based framework for classifying randomized controlled trial (RCT) results into six distinct categories positive, imprecise (+), neutral, inconclusive, negative, and harmful using confidence interval (CI) position relative to the minimal clinically important difference (MCID) as the primary tool, augmented by Bayesian posterior probabilities. We demonstrate that the same p > 0.05 result can represent three fundamentally different conclusions (inconclusive, negative, or neutral), show how Bayesian reanalysis can rescue benefit signals missed by frequentist thresholds, and illustrate the framework with real-world examples from critical care and cardiology trials. The framework synthesizes guidance from Altman, Harrell, Pocock, Zampieri, the ASA, and ICH E9 into a single coherent decision algorithm.