Minimal important change (MIC) based on a predictive modeling approach was more precise than MIC based on ROC analysis

Objectives: To present a new method to estimate a minimal important change (MIC) of health-related quality of life (HRQOL) scales, based on predictive modeling, and to compare its performance with the MIC based on receiver operating characteristic (ROC) analysis. To illustrate how the new method deals with variables that modify the MIC across subgroups. Study Design and Setting: The new method uses logistic regression analysis and identifies the change score associated with a likelihood ratio of 1 as the MIC. Simulation studies were conducted to investigate under which distributional circumstances both methods produce concordant or discordant results and whether the methods differ in accuracy and precision. Results: The predictive MIC and the ROC-based MIC were identical when the variances of the change scores in the improved and not-improved groups were equal and the distributions were normal or oppositely skewed. The predictive MIC turned out to be more precise than the ROC-based MIC. The predictive MIC allowed for the testing and estimation of modifying factors such as baseline severity. Conclusion: In many situations, the newly described MIC based on predictive modeling yields the same value as the ROC-based MIC but with significantly greater precision. This advantage translates to increased statistical power in MIC studies. (C) 2015 Elsevier Inc. All rights reserved.