Citation

Comins J, Brodersen J, Christensen KB, Jensen J, Hansen CF, Krogsgaard MR. Scand. J. Med. Sci. Sports 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, John Wiley and Sons)

DOI

10.1111/sms.13855

PMID

33063386

Abstract

Several terms are used to describe changes in PROM scores in relation to treatments. Whether the change is small, large, or relevant is defined in different ways, yet these change scores are used to recommend or oppose treatments. They are also used to calculate the necessary number of patients for a study. This article offers a theoretical explanation behind the terms responsiveness, minimal important difference (MID), minimal important change (MIC), minimal relevant difference (MIREDIF), and threshold of clinical importance. It also gives instructions on how these and the optimal number of patients for a study are calculated. Responses to two domains of the Knee Injury and Osteoarthritis Outcome Score (KOOS), before, and 1 year after reconstruction of the anterior cruciate ligament of 164 patients, are used to illustrate the calculations. This paper presents the most common methods used to calculate and interpret MID.

RESULTS vary substantially across domains, patient location on the scale, and health conditions. The optimal number of patients depends on the minimal relevant difference (MIREDIF), the standard error of the measure (SEM), the desired statistical power for the measurement, and the responsiveness of the measurement instrument (the PROM). There is often uncertainty surrounding the calculation and interpretation of responsiveness, MID, and MIREDIF, as these concepts are complex. When MID is used to evaluate research results, authors should specify how the MID was calculated, and its relevance for the study population. These measures should only be used after thorough consideration to justify healthcare decisions.

Language: en

Keywords

Difference; global rating of change; minimal important difference; minimal relevant; number of patients for a study; PROM; responsiveness