Performance of baseline quartile-stratified minimal clinically important difference estimates was superior to individual minimal clinically important difference estimates when compared with a gold standard comparator of important change

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Abstract
A variety of minimal clinically important difference (MCID) estimates are available to distinguish subgroups with differing outcomes. When a true gold standard is absent, latent class growth curve analysis (LCGC) has been proposed as a suitable alternative for important change. Our purpose was to evaluate the performance of individual and baseline quartile-stratified MCIDs. The current study included data from 346 persons with baseline and 12-month postoperative outcome data from KASTPain, a no-effect randomized clinical trial conducted on persons with knee arthroplasty and pain catastrophizing. Subgroup trajectories from LCGC were used as a gold standard comparator. Minimal clinically important difference–specific trajectories of recovery were calculated for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain, Disability and EuroQol-5 Dimension Visual Analogue Scale of self-reported health. The latent Kappa (K l ) chance-corrected agreement between MCIDs and LCGCs were estimated to indicate which MCID method was best at detecting important change. For all 3 outcomes, the average latent class probabilities ranged from 0.90 to 0.99, justifying the use of LCGCs as a gold standard. The K l for LCGC and individual MCIDs ranged from 0.21 (95% CI = 0.13, 0.28) to 0.52 (95% CI = 0.41, 0.66). Baseline quartile-stratified K l for WOMAC Pain and Disability were 0.85 (95% CI = 0.78, 0.92) and 0.74 (95% CI = 0.68, 0.83), respectively. Classification errors in individual MCID estimates most likely result from ceiling effects. Minimal clinically important differences calculated for each baseline quartile are superior to individually calculated MCIDs and should be used when latent class methods are not available. Use of individual MCIDs likely contribute substantial error and are discouraged for clinical application.
Funding Information
  • National Institutes of Health (UM1AR062800)
  • National Center for Advancing Translational Sciences (UL1TR000058)

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