Minimal clinically important difference
In the field of medical statistics, the minimal clinically important difference (also known as MCID), is a statistical model which tries to define the smallest change in a treatment outcome that a patient would identify as important.
Over the years great steps have been taken in reporting what really matters in clinical research. As was still common in the sixties a clinical researcher might report: “in my own experience treatment X does not do well for condition Y”. Conversely, as early as 1950 significance testing through 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 outcome. However, using patient reported outcome measurements 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) has been 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.
Methods of determining the MCID
Several techniques to calculate the MCID 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.
- Using the one-half standard deviation benchmark of an outcome measure entails that patient improving more than one-half of the outcome score’s standard deviation have achieved a minimal clinically important difference.
- The standard error of measurement is the variation in scores due to unreliability of the scale or measure used. Thus a change smaller than the standard error of measurement is likely to be the result of measurement error rather than a true observed change. Patients achieving a difference in outcome score of at least one standard error of measurement would have achieved a minimal clinically important difference.
- The effect size is a measure obtained by dividing the difference between the means of the baseline and posttreatment scores by the SD of the baseline scores. An effect size cut off point can be used to define MCID in the same way as the one half standard deviation and the standard error of measurement.
The anchor based method compares changes in scores with an “anchor” as reference. An anchor establishes if the patient is better after treatment compared to baseline according to the patients own experience. A popular anchor is the anchor question, at a specific point in time after treatment the patient might be asked: ‘‘Do you feel that you are improved by your treatment?’’. Answers to anchor questions could vary from a simple “yes” or “no”, to everything in between, e.g. “much better”, “slightly better”, “about the same”, “somewhat worse” and “much worse”. The latter one are part of the Medical Outcomes Study Short Form-36 which is a tool often used for assessing MCID. An interesting approach to the anchor based method is establishment of an anchor before treatment. The patient is asked what minimal outcome would be necessary to undergo the proposed treatment. This method allows for more personal variation as one patient might require more pain relief, where another strives towards more functional improvement. Different anchor questions and a different number of possible answers have been proposed.  Currently there is no consensus on the one right question nor on the best answers.
The Delphi method relies on a panel of experts who reach consensus regarding the MCID. The expert panel is provided with information on the results of a trial and are requested to provide their best estimate of the MCID. Their responses are averaged, and this summary is send back with an invitation to revise their estimates. This process is continued until consensus is achieved.
Anchor based method is not suitable for conditions where most patients will improve and few remain unchanged. High post treatment satisfaction results in insufficient discriminative ability for calculation of a MCID. A possible solution to this problem is a variation on the calculation of a 'substantial clinical benefit' score. This calculation is not based on the patients that improve vs. that do not, but on the patients that improve and those who improve a lot.
MCID calculation is of limited additional value for treatments that show effects only in the long run, e.g. tightly regulated blood glucose in the case of diabetes might cause discomfort because of the accompanying hypoglycemia (low blood sugar) and the perceived quality of life might actually decrease; however, regulation reduces severe long term complications and is therefore still warranted. The calculated MCID varies widely depending on the method used, currently there is no preferred method of establishing ‘the’ MCID.
The MCID varies according to diseases and outcome instruments, but it does not depend on treatment methods. Therefore, two different treatments for a similar disease can be compared using the same MCID if the outcome measurement instrument is the same. Also MCID seems to differ over time after treatment for the same disease.
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