This JAMA Guide to Statistics and Methods explains minimal clinically important difference, a concept in which clinicians define the smallest benefit of value to patients to help determine whether a therapy improves a subjective outcome enough from the perspective of the patient.
When assessing the clinical utility of therapies intended to improve subjective outcomes, the amount of improvement that is important to patients must be determined.1 The smallest benefit of value to patients is called the minimal clinically important difference (MCID). The MCID is a patient-centered concept, capturing both the magnitude of the improvement and also the value patients place on the change. Using patient-centered MCIDs is important for studies involving patient-reported outcomes,2 for which the clinical importance of a given change may not be obvious to clinicians selecting treatments. The MCID defines the smallest amount an outcome must change to be meaningful to patients.1
For example, Hinman et al3 reported findings from a clinical trial evaluating whether acupuncture (needle, laser, and sham laser) improved pain or overall functional outcomes compared with no acupuncture among patients with chronic knee pain. Pain was measured on a numerical rating scale and functional status by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score. The MCIDs for both end points were based on prior experience with these scoring systems. The MCID for pain was determined using an expert consensus, or Delphi approach,4 while the MCID for function was determined using an “anchor” approach, based on patients’ qualitative assessments of their own responses to treatment.5
The appropriate clinical interpretation of changes on a numerical scale must consider not only statistical significance, but also whether the observed change is meaningful to patients. Identical changes on a numerical scale may have different clinical importance in different patient populations (eg, different ages, disease severity, injury type). Furthermore, statistical significance is linked to the sample size. Given a large enough sample, statistical significance between groups may occur with very small differences that are clinically meaningless.6
When determining how many patients to enroll in a study, the calculation usually reflects the intention to reliably find a clinically important effect of a treatment, such as the MCID. The smaller the treatment effect sought, the larger the required number of study participants.7
The MCID can be calculated using consensus, anchor, and distribution-based methods. Consensus (also known as Delphi) methods convene an expert panel to provide independent assessments of what constitutes a clinically relevant change. The assessments are then revised after the panel members review each other's assessments. This process is repeated until a consensus is reached regarding a numerical value for the MCID. The MCID for the pain assessment scale used in Hinman et al3 was determined by ...