title: 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.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  Adjusting the predictive modeling MIC for the proportion of improved patients assures that the adjusted MIC reflects the gMIC.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of General Practice and Elderly Care Medicine, EMGO Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, Amsterdam 1081 BT, The Netherlands. Electronic address: b.terluin@vumc.nl.
authors.forename: Berend
authors.initials: B
authors.lastname: Terluin
authors.affiliation: Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands; Department of Child Health, Netherlands Organisation for Applied Scientific Research (TNO), Schipholweg 77-89, Leiden 2316 ZL, The Netherlands.
authors.forename: Iris
authors.initials: I
authors.lastname: Eekhout
authors.affiliation: Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Caroline B
authors.initials: CB
authors.lastname: Terwee
mesh_terms: D019985:Benchmarking; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011788:Quality of Life; D012372:ROC Curve; D016896:Treatment Outcome
keywords: Adjusted minimal important change; Minimal important change; Predictive modeling; Present state bias; Proportion improved patients; Receiver operating characteristics