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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2604.09108 |
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| _version_ | 1866918438441582592 |
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| author | Tanboga, Ibrahim Halil |
| author_facet | Tanboga, Ibrahim Halil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09108 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Practical Guide to Interpret a Randomized Controlled Trial Tanboga, Ibrahim Halil Methodology 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. |
| title | A Practical Guide to Interpret a Randomized Controlled Trial |
| topic | Methodology |
| url | https://arxiv.org/abs/2604.09108 |