Clinical StudyUnderstanding the minimum clinically important difference: a review of concepts and methods
Introduction
In everyday discourse, a significant difference is understood as a change that is important or meaningful. In the world of statistics, a significant difference is simply a difference that is unlikely to be caused by chance or happenstance and has a mathematical basis for such a claim. In the realm of health care, a difference may be statistically significant based on a simple numerical value, yet may at the same time be of little or no importance to the health or quality of life of patients afflicted by a certain disease. Furthermore, the size of the sample being tested will often contribute to the statistical significance of a given variable, such that a seemingly unimportant detail may gain apparent statistical significance. For example, most clinical trials and multicenter studies rely on large patient samples; small treatment effects may be identified as statistically significant. As a result, clinicians are left to decipher the importance of seemingly statistically significant results to their own patients.
The concept of a “clinically important difference” evolved as a way to overcome the shortcomings of the “statistically significant difference.” A clinically important difference represents a change that would be considered meaningful and worthwhile by the patient such that he/she would consider repeating the intervention if it were his/her choice to make again. The minimum clinically important difference (MCID) is a threshold value for such a change. Any amount of change greater than the MCID threshold is considered to be meaningful or important. Any patient whose answers allow them to reach the MCID threshold are considered “responders” [1]. The proportion of responders to total patients involved in a given treatment indicates to a clinician the likelihood of his/her patients also responding favorably to the same treatment [2].
The definition of an MCID would be particularly helpful in the evaluation of patient-reported outcomes (PROs). PROs refer to the patient's evaluation of a health condition and its treatment. PROs are generally used in medical research for three reasons [1]. First, the patient may be the only source of information. Some treatment effects can only be experienced by the patient. For instance, we rely entirely on a patient to assess physiological responses such as pain and nausea, for which there are no adequate observable or physical measures. Second, clinical measurements do not always match a patient's evaluation. Improvement in a clinical measure does not always correspond to an improvement in patient's pain and disability. For instance, the radiological evidence of bridging bone after spinal fusion surgery indicates a radiographically successful surgery. However, patients may still report pain and disability despite such evidence of bony fusion [3], [4]. Third, clinicians may obtain information known only to patients by directly asking questions of them. This is commonly done in clinical evaluations. PROs provide a structured and reproducible way to obtain this information. PROs are not biased by third-party interpretation and often remain reliable in studies in which significant interobserver bias may occur.
Patient reporting of outcomes has become an integral part of the evaluation of spinal interventions because back pain treatment primarily affects a patient's pain level, functional status, and overall quality of life [5]. Commonly used PRO measurements in spinal surgery research are pain scales, the Oswestry Disability Index, and the Short Form of the Medical Outcomes Study. No definitive MCID values have been established for these three PRO instruments in patients undergoing spinal surgery.
MCID was originally defined as “the smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient's management” [6]. This definition was later simplified to “the smallest change that is important to patients” [7]. MCID has recently been called “the new metric on the block” [8]. This review will describe the methods used to calculate MCID and their shortcomings. As it will be described, many issues are still unresolved in the determination of MCID, which render its new metrics status decidedly premature.
Section snippets
Determinations of MCID
Two general approaches have been used to determine MCID: anchor-based methods and distribution-based methods. All approaches measure a quantifiable change in outcomes, but the specific choice of approach will decide the type of change measured [9], [10].
Limitations of MCID determinations
Three main limitations remain in the methods of MCID definitions: each method produces a MCID value different from the other methods, MCID definitions do not take into account the cost of treatment to the patient, and the change in PRO scores depends on the patient initial baseline status.
Summary
Clearly, clinicians need a systematic way to assess the perceived benefit of a certain treatment based on individual patient improvement relative to both cost and risk of complications. MCID would ideally provide a specific threshold to serve as a treatment goal and is already used in that regard. For instance, MCID is included in the evaluation of clinical trials, but the Food and Drug Administration admits its need for further information about MCID [1]. The potential usefulness of MCID is to
Acknowledgment
The authors thank the members of the Lumbar Spine Study Group for their supported.
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Authors acknowledge a financial relationship (Medtronic), which may indirectly relate to the subject of this research.