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The objective of the study was to assess the usability of minimal important difference (MID) and minimal clinically important difference (MCID) for measuring meaningful changes in disease-specific and generic health-related quality-of-life (HRQoL) outcomes in patient-centered care. We adopted a two-step literature review process. First, we used PubMed and Google scholar to identify a broad range of search terms. Next, we searched OVID Medline, JSTOR, and PubMed for terms “MID,” and “MCID.” We excluded non-English language studies, articles older than 1995, those not related to generic- and disease-specific HRQoL measures, and protocols of future studies. Studies were grouped according to generic- and disease-specific measures. We assessed MID or MCID calculation methods, effect sizes, estimated values, and significance. Eighty articles satisfied the inclusion criteria. Our synthesis provides a comprehensive assessment of MID or MCID for 10 generic-specific and 80 disease-specific instruments. We observed a lack of consistency in the application of methods for computing MID or MCID for generic and disease-specific HRQoL measures. Only 43 (54%) studies used both anchor and distribution methods to elicit MID or MCID. Thirty-four articles estimated MID values only, whereas 47 articles estimated MCID. The anchor-based method yields conservative estimates of MID or MCID, compared to the distribution-based method. The distribution method does not take into account patient perspectives and should be accompanied by anchor method while computing MID. The MID should be interpreted with caution, and available estimates for a particular instrument must be used. This will help in integrating the MID estimates into the overall research or clinical plan for a specific context.

PurposeAnchor-based studies are today the most popular approach to determine a minimal important difference value for an outcome variable. However, a variety of construction methods for such values do exist. This constitutes a challenge to the field. In order to distinguish between more or less adequate construction methods, meaningful minimal requirements can be helpful. For example, minimal important difference values should not reflect the intervention(s) the patients are exposed to in the study used for construction, as they should later allow to compare interventions. This requires that they are not sensitive to the distribution of the change score observed. This study aims at investigating to which degree established construction methods fulfil this minimal requirement.MethodsSix constructions methods were considered, covering very popular and recently suggested methods. The sensitivity of MID values to the distribution of the change score was investigated in a simulation study for these six construction methods.ResultsFive out of six construction methods turned out to yield MID values which are sensitive to the distribution of the change score to a degree that questions their usefulness. Insensitivity can be obtained by using construction methods based solely on an estimate of the conditional distribution of the anchor variable given the change score.ConclusionIn future the computation of MID values should be based on construction methods avoiding sensitivity to the distribution of the change score.

Background Total knee arthroplasty (TKA) is an effective treatment for end-stage osteoarthritis. Patient reported-outcome measures (PROMs) capture the patients’ perception of the success of an intervention. The minimal important difference (MID) is an important characteristic of the PROM, which helps to interpret results. The aim of this study was to identify the MID for the Forgotten Joint Score-12 (FJS-12) and Western Ontario and McMaster Universities (WOMAC) osteoarthritis index. Methods Data were collected in a prospective cohort study. Patients were asked to complete the FJS-12, WOMAC osteoarthritis index and transition items evaluating change over time to determine the MID. We employed an anchor-based methodology relating score change to the response categories of the transition items using both binary logistic regression and receiver operating characteristic (ROC) analysis. Results Data from 199 patients were analysed. Mean age was 72.3 years, 58% were women. Employing binary logistic regression the MID for the FJS-12 was 10.8 points, for the WOMAC pain score 7.5 points and for the WOMAC function score 7.2 points. ROC analyses found a MID of 13.0 points for the FJS-12, 12.5 points for WOMAC pain and 14.7 points for WOMAC function. Conclusion We report MIDs for the FJS-12 and the WOMAC Pain and Function scales in a TKA patient cohort, which can be used to interpret meaningful differences in score. In line with previous research, we found more advanced statistical methods to result in smaller MID estimates for both scores. Trial registration Written consent for this study was obtained from all participants and ethical approval was granted by the local ethics committee (Ethikkommission St. Gallen; EKSG 14/973; Registered 03 July 2014; http://www.sg.ch/home/gesundheit/ethikkommission.html ).

The minimal important difference (MID) is a phrase with instant appeal in a field struggling to interpret health-related quality of life and other patient-reported outcomes. The terminology can be confusing, with several terms differing only slightly in definition (e.g., minimal clinically important difference, clinically important difference, minimally detectable difference, the subjectively significant difference), and others that seem similar despite having quite different meanings (minimally detectable difference versus minimum detectable change). Often, nuances of definition are of little consequence in the way that these quantities are estimated and used. Four methods are commonly employed to estimate MIDs: patient rating of change (global transition items); clinical anchors; standard error of measurement; and effect size. These are described and critiqued in this article. There is no universal MID, despite the appeal of the notion. Indeed, for a particular patient-reported outcome instrument or scale, the MID is not an immutable characteristic, but may vary by population and context. At both the group and individual level, the MID may depend on the clinical context and decision at hand, the baseline from which the patient starts, and whether they are improving or deteriorating. Specific estimates of MIDs should therefore not be overinterpreted. For a given health-related quality-of-life scale, all available MID estimates (and their confidence intervals) should be considered, amalgamated into general guidelines and applied judiciously to any particular clinical or research context.

Purpose The Knee Society Clinical Rating System (KS) is one of the most popular tools used to assess patient outcome after total knee arthroplasty (TKA), but its minimal clinically important difference (MCID) has not been identified. This study aims to identify the MCID of KS function score (KS-FS) and knee score (KS-KS) after TKA in patients with primary knee osteoarthritis. Methods The authors retrospectively analysed patients who underwent TKA for primary knee osteoarthritis between 2005 and 2015 in a single institution. KS-FS, KS-KS, and Oxford Knee Score (OKS) were collected pre-operatively and 2 years post-operatively. Patient satisfaction with TKA at 2 years was also collected. Anchor-based approach with 2 external indicators was used. The MCID for KS-FS and KS-KS was determined using simple linear regression according to patient satisfaction with TKA and the MCID of OKS. Results The mean age of the 550 subjects studied was 66 ± 8 years. There were 373 (67.8 %) female subjects. The KS-FS improved by 22.8 (95 % CI 20.9–24.6) points, and the KS-KS improved by 44.4 (95 % CI 42.6–46.3) points. The MCID identified for KS-FS is between 6.1 (95 % CI 5.1–7.1) and 6.4 (95 % CI 4.4–8.4) and between 5.3 (95 % CI 4.3–6.3) and 5.9 (95 % CI 3.9–7.8) for KS-KS. Conclusions This is the first study, to the knowledge of the authors, to identify the MCID of KS. This will allow future trials to have an accurate prediction of sample size. Clinically, physicians will be able to better interpret outcomes of TKA studies to guide a treatment option. Level of evidence IV.

Background Using a real dataset, we highlighted several major methodological issues raised by the estimation of the Minimal Clinically Important Difference (MCID) of a Patient-Reported Outcomes instrument. We especially considered the management of missing data and the use of more than two times of measurement. While inappropriate missing data management and inappropriate use of multiple time points can lead to loss of precision and/or bias in MCID estimation, these issues are almost never dealt with and require cautious considerations in the context of MCID estimation. Methods We used the LIGALONGO study (French Randomized Controlled Trial). We estimated MCID on the SF-36 General Health score by comparing many methods (distribution or anchor-based). Different techniques for imputation of missing data were performed (simple and multiple imputations). We also consider all measurement occasions by longitudinal modeling, and the dependence of the score difference on baseline. Results Three hundred ninety-three patients were studied. With distribution-based methods, a great variability in MCID was observed (from 3 to 26 points for improvement). Only 0.2 SD and 1/3 SD distribution methods gave MCID values consistent with anchor-based methods (from 4 to 7 points for improvement). The choice of missing data imputation technique clearly had an impact on MCID estimates. Simple imputation by mean score seemed to lead to out-of-range estimate, but as missing not at random mechanism can be hypothesized, even multiple imputations techniques can have led to an slight underestimation of MCID. Using 3 measurement occasions for improvement led to an increase in precision but lowered estimates. Conclusion This practical example illustrates the substantial impact of some methodological issues that are usually never dealt with for MCID estimation. Simulation studies are needed to investigate those issues. Trial registration NCT01240772 (ClinicalTrials.gov) registered on November 15, 2010.

Purpose Meaningful change thresholds are important to help interpret patient-reported outcome scores. To date, meaningful within-patient change (MWPC) thresholds have only been proposed for NSCLC-SAQ total score. This study proposed clinically MWPC thresholds, and group-level minimal important change/difference (MIC/MID) thresholds for both improvement and worsening for the Non-Small Cell Lung Cancer- Symptom Assessment Questionnaire (NSCLC-SAQ) total and symptom scores. Methods Blinded patient data ( N  = 246) from the Phase 3 LIBRETTO-431 clinical trial were used in a pre-specified meaningful change analysis. A combination of anchor- and supportive distribution-based methods were used to estimate the MWPC, MIC, and MID thresholds. Triangulation across anchor estimates was then performed using a correlation-weighted average to provide a single MWPC, MIC, and MID estimate for improvement and worsening. Results NSCLC-SAQ total score and symptom scores showed moderate to high correlations with various anchors (ranging from 0.306 to 0.890), with threshold estimates being provided from multiple anchors (except for cough). Triangulation suggested MWPC, MIC, and MID thresholds for improved total score were − 2.5, -3.5, and − 2.0, respectively. For worsening, the proposed thresholds were 2.0, 0.5, and 2.0, respectively. The MWPC, MIC, and MID thresholds for improved symptom scores ranged from − 0.5 to -1.5, and the worsening thresholds for symptom scores ranged from 0.5 to 1.0. Conclusion This study provides the first worsening and improvement estimates of MWPC, MIC, and MID for NSCLC-SAQ total and symptom scores. The thresholds proposed in this study can be used to inform interpretation of NSCLC-SAQ scores in clinical trials.

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.

Background Patient-Reported Outcomes (PROs) are standardized questionnaires used to measure subjective outcomes such as quality of life in healthcare. They are considered paramount to assess the results of therapeutic interventions. However, because their calibration is relative to internal standards in people’s mind, changes in PRO scores are difficult to interpret. Knowing the smallest value in the score that the patient perceives as change can help. An estimator linking the answers to a Patient Global Rating of Change (PGRC: a question measuring the overall feeling of change) with change in PRO scores is frequently used to obtain this value. In the last 30 years, a plethora of methods have been used to obtain these estimates, but there is no consensus on the appropriate method and no formal definition of this value. Methods We propose a model to explain changes in PRO scores and PGRC answers. Results A PGRC measures a construct called the Perceived Change (PC), whose determinants are elicited. Answering a PGRC requires discretizing a continuous PC into a category using threshold values that are random variables. Therefore, the populational value of the Minimal Perceived Change (MPC) is the location parameter value of the threshold on the PC continuum defining the switch from the absence of change to change. Conclusions We show how this model can help to hypothesize what are the appropriate methods to estimate the MPC and its potential to be a rigorous theoretical basis for future work on the interpretation of change in PRO scores.