Linking Minimally Important Differences (MID) and Acceptable Regret to Elicit Values and Preferences in Health Decision Models.

RATIONALE, AIMS AND

objectivesMost methods for elicitation of values and preferences (V&P) ask respondents directly to make explicit numerical trade-offs across outcomes, such as judging how many strokes equal one death. Although conceptually straightforward, these tasks are cognitively demanding, uncomfortable for many patients and panellists, and prone to instability when multiple outcomes must be compared. This paper proposes an integrated framework that starts with minimally important differences (MIDs), links them to acceptable regret, and then converts them into relative values (RVs) for use as V&P in decision-analytical models.

methodsWe describe a three-step approach. First, respondents identify the smallest absolute change in outcome frequency that would be important enough to justify a different decision. Second, acceptable regret is used to interpret and calibrate these thresholds as the amount of utility loss from a wrong decision that patients can tolerate. Third, MIDs are transformed into RVs on a common scale anchored to a worst outcome, usually death, and entered as V&P into a weighted disutility expected utility model.

resultsThe resulting framework is designed to replace difficult, cognitively demanding elicitation of V&P with simpler threshold judgements, maintains proportional relationships among outcomes regardless of the chosen anchor, reduces the number of required judgements, and yields internally consistent weights for multiple benefits and harms. A worked example shows how MID thresholds for death, stroke, myocardial infarction, major bleeding and brain bleeding are converted into RVs and then incorporated into a transparent benefit-harm calculation.

conclusionsIntegrating MIDs, acceptable regret and RVs offer a coherent approach to eliciting values and preferences for clinical decision-making, guideline development and health policy. The approach is easy to explain, well aligned with human decision processes and readily applicable at both individual and population levels.