Deconstructing the Minimum Clinically Important Difference (MCID)

PURPOSE: The minimal clinically important difference (MCID) is a way of dichotomizing data for assessment of success or failure based on clinically meaningful changes. The magnitude of the MCID is often misunderstood to be a singular quantity applicable across studies. However, substantial differences have been reported among MCIDs for the same outcome measures usually based upon differences extrinsic to the calculation. This study explores the effects of variabilities intrinsic to the calculation of the MCID. METHODS: The MCIDs for two knee replacement patient-reported outcomes measures of pain and function were calculated at 1 year postoperative with an integrative anchor and distribution-based method using external anchor questions and receiver operator characteristic (ROC) curves. The effects upon the magnitude and precision of the MCIDs of varying the anchor questions, the thresholds for success/failure, and the sample sizes were examined. RESULTS: Wide variabilities were observed in both the magnitudes and precision of the MCIDs. The threshold for success had the largest effect on magnitude of pain scores, while the sample size had the largest effect on precision. For function scores, the sample size had the largest effect on magnitude, and the anchor question had the largest effect on precision. CONCLUSION: Comparisons among MCIDs are difficult to interpret if elements of the calculations are different and influence the results. While factors extrinsic to the calculations, e.g., study population, trial design, methods of calculation, etc., are known to produce differences in the magnitude of MCIDs, this study shows that more subtle and less obvious factors intrinsic to the calculations have profound effects on both the magnitude and precision of MCIDs. Comparisons among MCIDs should be made with caution and call for greater transparency in reporting intrinsic methods. It is probably advisable for individual studies to calculate their own MCIDs and not rely on published values.