Improving clinical trial interpretation with ACCEPT analyses

Michelle N. Clements; Ian R. White; Andrew J. Copas; Victoria Cornelius; Suzie Cro; David T Dunn; Matteo Quartagno; Rebecca M. Turner; Conor D. Tweed; A. Sarah Walker

arXiv · 2203.11164

Improving clinical trial interpretation with ACCEPT analyses

Michelle N. Clements·Ian R. White·Andrew J. Copas·Victoria Cornelius·Suzie Cro·David T Dunn·Matteo Quartagno·Rebecca M. Turner·Conor D. Tweed·A. Sarah Walker

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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.

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Michelle N. Clements, Ian R. White, Andrew J. Copas, Victoria Cornelius, Suzie Cro, David T Dunn, Matteo Quartagno, Rebecca M. Turner, Conor D. Tweed, A. Sarah Walker. 2022-03-21. Improving clinical trial interpretation with ACCEPT analyses. https://arxiv.org/abs/2203.11164

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