Minimal clinically important difference

The minimal important difference (MID), is the smallest change in a treatment outcome that a patient would identify as important and which would mandate a change in the patient's management.

Over the years great steps have been taken in reporting what really matters in clinical research. A clinical researcher might report: “in my own experience treatment X does not do well for condition Y”. The use of a P value cut off point of 0.05 was introduced by R.A. Fisher; this resulted in studies being either significant or non-significant. Although this P value objectified research outcome, retaining to such a rigid cut off point can have two potentially serious consequences: (i) possibly clinically important differences observed in studies can be denoted as statistically non-significant and therefore be unfairly ignored as a result of having a small number of subjects studied (type II errors); (ii) even the smallest difference in measurements can be proved statistically significant by increasing the number of subjects in a study. Such a small difference could be irrelevant (i.e. of no clinical importance) to patients or clinicians. Thus, statistical significance does not necessarily imply clinical importance. Over the years clinicians and researchers have moved away from physical and radiological endpoints towards patient-reported outcomes. However, using patient-reported outcomes does not solve the problem of small differences being statistical significance but possibly clinically irrelevant. In order to study clinical importance, the concept of minimal clinically important difference (MCID) was proposed by Jaesche et al. in 1989. MCID is the smallest change in an outcome that a patient would identify as important. MCID therefore offers a threshold above which outcome is experienced as relevant by the patient; this avoids the problem of mere statistical significance. Schunemann and Guyatt recommending minimally important difference (MID) to remove the "focus on 'clinical' interpretations" (2005, p. 594).

Several techniques to calculate the MID have been described and can be subdivided in roughly three categories: distribution-based methods, anchor-based methods and the Delphi method. There is no consensus regarding the optimal technique, but distribution-based methods have been criticized. For example, use of the standard error of the mean (SEM) is based on anecdotal observations that it is approximately equal to 1/2 SD when the reliability is 0.75. But Revicki et al. question why 1 SEM should "have anything to do with the MID? The SEM is estimated by the product of the SD and the square root of 1-reliability of a measure. The SEM is used to set the confidence interval (CI) around an individual score, that is, the observed score plus or minus 1.96 SEMS constitutes the 95% CI. In fact, the reliable change index proposed early by Jacobson and Truax [12] is based on defining change using the statistical convention of exceeding 2 standard errors" (p. 106).

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