The anchor-based minimal important change, based on receiver operating characteristic analysis or predictive modeling, may need to be adjusted for the proportion of improved patients

J Clin Epidemiol. 2017 Mar;83:90-100. doi: 10.1016/j.jclinepi.2016.12.015. Epub 2017 Jan 14.

Abstract

Objectives: Patients have their individual minimal important changes (iMICs) as their personal benchmarks to determine whether a perceived health-related quality of life (HRQOL) change constitutes a (minimally) important change for them. We denote the mean iMIC in a group of patients as the "genuine MIC" (gMIC). The aims of this paper are (1) to examine the relationship between the gMIC and the anchor-based minimal important change (MIC), determined by receiver operating characteristic analysis or by predictive modeling; (2) to examine the impact of the proportion of improved patients on these MICs; and (3) to explore the possibility to adjust the MIC for the influence of the proportion of improved patients.

Study design and setting: Multiple simulations of patient samples involved in anchor-based MIC studies with different characteristics of HRQOL (change) scores and distributions of iMICs. In addition, a real data set is analyzed for illustration.

Results: The receiver operating characteristic-based and predictive modeling MICs equal the gMIC when the proportion of improved patients equals 0.5. The MIC is estimated higher than the gMIC when the proportion improved is greater than 0.5, and the MIC is estimated lower than the gMIC when the proportion improved is less than 0.5. Using an equation including the predictive modeling MIC, the log-odds of improvement, the standard deviation of the HRQOL change score, and the correlation between the HRQOL change score and the anchor results in an adjusted MIC reflecting the gMIC irrespective of the proportion of improved patients.

Conclusion: Adjusting the predictive modeling MIC for the proportion of improved patients assures that the adjusted MIC reflects the gMIC.

Limitations: We assumed normal distributions and global perceived change scores that were independent on the follow-up score. Additionally, floor and ceiling effects were not taken into account.

Keywords: Adjusted minimal important change; Minimal important change; Predictive modeling; Present state bias; Proportion improved patients; Receiver operating characteristics.

MeSH terms

  • Benchmarking
  • Data Interpretation, Statistical
  • Humans
  • Models, Statistical*
  • Quality of Life
  • ROC Curve*
  • Treatment Outcome*