Improving clinical trial interpretation with ACCEPT analyses

TL;DR

ACCEPT analyses improve clinical trial interpretation by harmonizing result reporting, enabling better comparisons across trials, and focusing on data interpretation rather than just statistical significance.

Contribution

This paper introduces ACCEPT, a novel method for standardizing and interpreting clinical trial results across different study designs.

Findings

ACCEPT facilitates comparison of trials with different designs.

The online tool allows simultaneous analysis of up to three trials.

ACCEPT enhances understanding of trial data beyond traditional p-value interpretation.

Abstract

Effective decision making from randomised controlled clinical trials relies on robust interpretation of the numerical results. However, the language we use to describe clinical trials can cause confusion both in trial design and in comparing results across trials. ACceptability Curve Estimation using Probability Above Threshold (ACCEPT) aids comparison between trials (even where of different designs) by harmonising reporting of results, acknowledging different interpretations of the results may be valid in different situations, and moving the focus from comparison to a pre-specified value to interpretation of the trial data. ACCEPT can be applied to historical trials or incorporated into statistical analysis plans for future analyses. An online tool enables ACCEPT on up to three trials simultaneously.