🤖 AI Summary
This study addresses the challenge of jointly weighing loss aversion, adverse effects, and implementation costs in clinical or public health intervention decisions. Methodologically, it proposes a formal Bayesian utility-theoretic decision framework that integrates posterior distribution inference with a structured, non-arbitrary 1–9 value scale to map effect size, inter-individual response variability (standard deviation), and multidimensional costs onto a unified expected utility score. The framework further incorporates credible intervals, sensitivity analyses, and estimation of response-type proportions (benefit, no effect, harm). Its primary contributions are: (1) establishing the first comparable, interpretable, and operationally actionable quantitative utility paradigm for intervention evaluation; (2) generating both a single composite utility score and a full population-level response distribution; and (3) implementing the framework in an accessible spreadsheet tool, thereby providing a transparent, robust, and user-friendly aid for evidence-informed decision-making.
📝 Abstract
This article presents a pragmatic framework for making formal, utility-based decisions from statistical inferences. The method calculates an expected utility score for an intervention by combining Bayesian posterior probabilities of different effect magnitudes with points representing their practical value. A key innovation is a unified, non-arbitrary points scale (1-9 for small to extremely large) derived from a principle linking tangible outcomes across different effect types. This tangible scale enables a principled"trade-off"method for including values for loss aversion, side effects, and implementation cost. The framework produces a single, definitive expected utility score, and the initial decision is made by comparing the magnitude of this single score to a user-defined smallest important net benefit, a direct and intuitive comparison made possible by the scale's tangible nature. This expected utility decision is interpreted alongside clinical magnitude-based decision probabilities or credible interval coverage to assess evidence strength. Inclusion of a standard deviation representing individual responses to an intervention (or differences between settings with meta-analytic data) allows characterization of differences between individuals (or settings) in the utility score expressed as proportions expected to experience benefit, a negligible effect, and harm. These proportions provide context for the final decision about implementation. Users must perform sensitivity analyses to investigate the effects of systematic bias and of the subjective inputs on the final decision. This framework, implemented in an accessible spreadsheet, has not been empirically validated. It represents a tool in development, designed for practical decision-making from available statistical evidence and structured thinking about values of outcomes.