Toward a rigorous assessment of the statistical performances of methods to estimate the Minimal Important Difference of Patient-Reported Outcomes: A protocol for a large-scale simulation study.

Interpreting observed changes over time in Patient-Reported Outcomes (PRO) measures is still considered a challenge. Indeed, concluding an observed change at group level is statistically significant does not necessarily equate this change is meaningful from the perspective of the patient. To help interpret within and/or between group changes in the measure over time, the estimation of the Minimal Important Difference (MID) of the instrument - the smallest value that patients consider as a perceived change - is useful. In the last 30 years, a plethora of methods and estimators have been proposed to derive this MID value using clinical data from sample of patients. MIDs for hundreds of PROs have been estimated, with frequently a substantial variability in the results depending on the method used. Nonetheless, a rigorous assessment of the statistical performances of numerous proposed methods for estimating MIDs by experimental design such as Monte-Carlo study has never been performed. The purpose of this paper is to thoroughly depict a protocol for a large-scale simulation study designed to investigate the statistical performances, especially bias against a true populational value, of the common proposed estimators for MID. This paper depicts how investigated methods and estimators were retained after the conduct of a systematic review, the design of a conceptual model that formally defines what is the true populational MID value and the translation of the conceptual model into a model allowing the simulation of responses of items to a hypothetical PRO at two times of measurement along with the response to a Patient Global Rating of Change at the second time under the constraint of a known true MID value. A statistical analysis plan is depicted in order to conclude if working hypotheses on what could be appropriate MID estimators will be verified. Strengths, assumptions, and limits of the simulation model are exposed. Finally, we show how this protocol could be the basis for fostering future methodological research on the issue of interpreting changes in PRO measures.