Anchored Minimal Clinically Important Difference Metrics: Considerations for Bias and Regression to the Mean.

Abstract

Minimal clinically important differences (MCIDs) are used to understand clinical relevance. However, repeated observations produce biased analyses unless one accounts for baseline observation, known as regression to the mean (RTM). Using an International Knee Documentation Committee (IKDC) survey dataset, we can demonstrate the effect of RTM on MCID values by (1) MCID-estimate dependence on baseline observation and (2) MCID-estimate bias being higher when the posttest-pretest data correlation is lower. We created 10 IKDC datasets with 5000 patients and a specific correlation under both equal and unequal variances. For each 10-point increase in baseline IKDC, MCID decreased by 3.5, 2.7, 1.9, 1.2, and 0.7 points when posttest-pretest correlations were 0.10, 0.25, 0.50, 0.75, and 0.90, respectively, under equal variances. Not accounting for RTM resulted in a static 20-point MCID. Minimal clinically important difference estimates may be unreliable. Minimal clinically important difference calculations should include the correlation and variances between posttest and pretest data, and researchers should consider using a baseline covariate-adjusted receiver operating characteristic curve analysis to calculate MCID.