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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. 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. 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. 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.

Background As one of the main goals of hip and knee replacements is to improve patients’ health-related quality of life, a meaningful evaluation can be achieved by calculating minimal important changes (MICs) for improvements in patient-reported outcome measures (PROMs). This study aims at providing MICs adjusted for patient characteristics for EQ-5D-3L index score improvements after hip and knee replacements. It adds to existing literature by relying on a large national sample and precise clustering algorithms, and by employing a state-of-the-art methodology for the calculation of improved adjusted MICs. Methodology A retrospective observational study was conducted using the publicly available National Health Service (NHS) PROMs dataset for primary hip and knee replacements. We used information on 252,331 hip replacements and 279,668 knee replacements from all NHS-funded providers in England between 2013 and 2020. Clusters of patients were created based on pre-operative EQ-VAS, depression status, and sex. Unstratified and stratified estimates for meaningful EQ-5D-3L improvements were obtained through anchor-based predictive MICs corrected for the proportion of improved patients and the reliability of transition ratings. Results Stratifying patients showed that MICs varied across subgroups based on pre-operative EQ-VAS, depression status, and sex. MICs were larger for patients with worse pre-operative EQ-VAS scores, while patients with better pre-operative scores required smaller MICs to achieve a meaningful change. We show how after stratification the percentage of patients achieving their stratified MIC was better in line with the actual share of improved patients. Larger MICs were found for patients with depression and for female patients. MICs calculated for knee replacements were consistently lower than those for hip replacements. Conclusions Our findings show the importance of adjusting MICs for patients’ characteristics and should be considered for quality-related choices and policy initiatives.

We define the minimal important change (MIC) as a threshold for a minimal within-person change over time above which patients perceive themselves importantly changed. There is a lot of confusion about the concept of MIC, particularly about the concepts of minimal important change and minimal detectable change, which questions the validity of published MIC values. The aims of this study were: (1) to clarify the concept of MIC and how to use it; (2) to provide practical guidance for estimating methodologically sound MIC values; and (3) to improve the applicability of PROMIS by summarizing the available evidence on plausible PROMIS MIC values. We discuss the concept of MIC and how to use it and provide practical guidance for estimating MIC values. In addition, we performed a systematic review in PubMed on MIC values of any PROMIS measure from studies using recommended approaches. A total of 50 studies estimated the MIC of a PROMIS measure, of which 19 studies used less appropriate methods. MIC values of the remaining 31 studies ranged from 0.1 to 12.7 T-score points. We recommend to use the predictive modeling method, possibly supplemented with the vignette-based method, in future MIC studies. We consider a MIC value of 2–6 T-score points for PROMIS measures reasonable to assume at this point. For surgical interventions a higher MIC value might be appropriate. We recommend more high-quality studies estimating MIC values for PROMIS.

To present estimates of clinically meaningful or minimal important changes for the Oxford Hip Score (OHS) and the Oxford Knee Score (OKS) after joint replacement surgery. Secondary data analysis of the NHS patient-reported outcome measures data set that included 82,415 patients listed for hip replacement surgery and 94,015 patients listed for knee replacement surgery was performed. Anchor-based methods revealed that meaningful change indices at the group level [minimal important change (MIC)], for example in cohort studies, were ∼11 points for the OHS and ∼9 points for the OKS. For assessment of individual patients, receiver operating characteristic analysis produced MICs of 8 and 7 points for OHS and OKS, respectively. Additionally, the between group minimal important difference (MID), which allows the estimation of a clinically relevant difference in change scores from baseline when comparing two groups, that is, for clinical trials, was estimated to be ∼5 points for both the OKS and the OHS. The distribution-based minimal detectable change (MDC90) estimates for the OKS and OHS were 4 and 5 points, respectively. This study has produced and discussed estimates of minimal important change/difference for the OKS/OHS. These estimates should be used in the power calculations and the interpretation of studies using the OKS and OHS. The MDC90 (∼4 points OKS and ∼5 points OHS) represents the smallest possible detectable change for each of these instruments, thus indicating that any lower value would fall within measurement error.

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. 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. 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. 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.

The anchor-based minimal important change (MIC), based on the receiver operating characteristic (ROC) analysis or predictive modeling, is biased by the proportion of improved patients. The adjusted MIC, published in 2017, adjusts the predictive MIC for this bias but does not take the reliability of the transition ratings (i.e., the anchor) into account. The aim of this study was to examine whether the transition ratings reliability affects the accuracy of the adjusted MIC and, if so, whether the adjustment can be improved. Multiple simulations of patient samples involved in anchor-based MIC studies with different characteristics of patient-reported outcome scores were used to determine the impact of reliability of the transition ratings on the MIC estimate. An improved adjustment formula was derived in an exploration set of simulated samples (number of samples = 19,440) and validated in a different set of simulated samples (number of samples = 12,960). The effect of sample size (100–1,000) was also evaluated in simulated datasets. Reliability of the transition ratings biased the MIC estimate if the proportion improved was different from 0.5. The improved adjustment formula performed well, especially if the proportion improved was between 0.3 and 0.7. Smaller sample sizes were at the expense of the precision of the MIC estimates. We provide an improved formula for calculating the adjusted MIC, taking into account the proportion of improved patients and the reliability of the transition ratings.

Purpose Thresholds for meaningful within-individual change (MWIC) are useful for interpreting patient-reported outcome measures (PROM). Transition ratings (TR) have been recommended as anchors to establish MWIC. Traditional statistical methods for analyzing MWIC such as mean change analysis, receiver operating characteristic (ROC) analysis, and predictive modeling ignore problems of floor/ceiling effects and measurement error in the PROM scores and the TR item. We present a novel approach to MWIC estimation for multi-item scales using longitudinal item response theory (LIRT). Methods A Graded Response LIRT model for baseline and follow-up PROM data was expanded to include a TR item measuring latent change. The LIRT threshold parameter for the TR established the MWIC threshold on the latent metric, from which the observed PROM score MWIC threshold was estimated. We compared the LIRT approach and traditional methods using an example data set with baseline and three follow-up assessments differing by magnitude of score improvement, variance of score improvement, and baseline-follow-up score correlation. Results The LIRT model provided good fit to the data. LIRT estimates of observed PROM MWIC varied between 3 and 4 points score improvement. In contrast, results from traditional methods varied from 2 to 10 points—strongly associated with proportion of self-rated improvement. Best agreement between methods was seen when approximately 50% rated their health as improved. Conclusion Results from traditional analyses of anchor-based MWIC are impacted by study conditions. LIRT constitutes a promising and more robust analytic approach to identifying thresholds for MWIC.

Background Appropriately defining and using the minimal important change (MIC) and the minimal clinically important difference (MCID) are crucial for determining whether the results are clinically significant. The aim of this study is to survey the status of randomized controlled trials (RCTs) for insomnia interventions to assess the inclusion and interpretation of MIC/MCID values. Methods We conducted a cross-sectional study to survey the status of RCTs for insomnia interventions to assess the inclusion and appropriate interpretation of MIC/MCID values. A literature search was conducted by searching the main sleep medicine journals indexed in PubMed, the Excerpta Medica Database (EMBASE), and the Cochrane Central Register of Controlled Trials (CENTRAL) to identify a broad range of search terms. We included RCTs with no restriction on the intervention. The included studies used the Insomnia Severity Index (ISI) or the Pittsburgh Sleep Quality Index (PSQI) questionnaire as the outcome measures. Results 81 eligible studies were identified, and more than one-third of the included studies used MIC/MCID ( n  = 31, 38.3%). Among them, 21 studies with ISI as the outcome used MIC defined as a relative decrease ranging from 3 to 8 points. The most frequently used MIC value was a 6-point decrease ( n  = 7), followed by 8-point ( n  = 6) and 7-point decrease ( n  = 4), a 4 to 5-points decrease ( n  = 3), and a 30% reduction from baseline; 6 studies used MCID values, ranging from 2.8 to 4 points. The most frequently used MCID value was a 4-point decrease in the ISI ( n  = 4). 4 studies with PSQI as the outcome used a 3-point change as the MIC ( n  = 2) and a 2.5 to 2.7-point difference as MCID ( n  = 2). 4 non-inferiority design studies considered interval estimation when drawing clinically significant conclusions in their MCID usage. Conclusions The lack of consistent MIC/MCID interpretation and usage in outcome measures for insomnia highlights the urgent need for further efforts to address this issue and improve reporting practices.

Purpose To facilitate the interpretation of empirical results produced by the 15D, a generic, preference-based instrument for measuring health-related quality of life (HRQoL), a subjective five-category global assessment scale (GAS) was used as an external anchor to determine the minimum important change (MIC) in the 15D scores. Methods Altogether 4,903 hospital patients representing sixteen disease entities and having the 15D score at baseline repeated the HRQoL assessment at 6 months after treatment and answered the question: compared to the situation before treatment, my overall health status is now (1) much better, (2) slightly better, (3) much the same, (4) slightly worse, (5) much worse. Regression analysis was used to estimate the MIC for improvement/deterioration, defined as the lower/upper limit of 99.9 % confidence interval of the regression coefficient, standardized for baseline HRQoL, for categories (2) and (4), respectively, and confirmed by ROC curve analysis. Results The limits or intervals for classifying the changes of 15D scores into GAS categories were >.035 for (1), .015–.035 for (2),>–.015 and<.015 for (3), –.035 –.015 for (4) and <–.035 for (5). The lower/upper limits of ±.015 for categories (2) and (4) can be regarded as the generic MIC thresholds for improvement/deterioration, respectively. Conclusions The generic MICs for the change of 15D scores are ±.015. Follow-up studies using the 15D should report the mean change in the 15D score, its statistical significance, relationship to the MIC, and the distribution of the changes of the 15D scores into the five categories.