Beyond the P Value: A Systematic Framework for Interpreting Oncology Clinical Trials
TL;DR
A structured framework to evaluate late-phase trials beyond simple P value assessments is developed and empowers researchers to rescue therapies with latent clinical benefit or confidently confirm futility, thereby optimizing the evidence landscape for precision oncology.
Abstract
In oncology, interpreting clinical trials solely through statistical significance (P < .05) often conflates true biological futility with methodological false negatives. This binary oversimplification risks prematurely abandoning active therapies while wasting resources on futile programs. We aimed to develop a structured framework to evaluate late-phase trials beyond simple P value assessments. Through a critical review of literature and landmark late-phase oncology trials, we analyzed common failure mechanisms and methodological pitfalls to construct a comprehensive, three-step methodology for the post hoc evaluation of negative studies. The review yielded a synthesized framework that approaches trial interpretation through three sequential steps. First, classification stratifies trials into five distinct categories: true negatives, false negatives, inconclusive trials, positive but irrelevant results, and nonsuperior but clinically valuable. Second, diagnosis uses root-cause analysis to identify underlying trial design flaws, execution biases, or statistical pitfalls. Third, recommendations outline targeted actionable strategies, including trial redesign, supplementary biomarker validation, precision medicine approaches, or scientific confirmation of therapeutic futility. This framework shifts trial interpretation from a simple win/loss assessment to a nuanced, value-based strategy. By systematically dissecting the root causes of trial failures, it empowers researchers to rescue therapies with latent clinical benefit or confidently confirm futility, thereby optimizing the evidence landscape for precision oncology.