Practical issues encountered while determining Minimal Clinically Important Difference in Patient-Reported Outcomes

Abstract

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.

Short Physical Performance Battery: Response to Pulmonary Rehabilitation and Minimal Important Difference Estimates in Patients With Chronic Obstructive Pulmonary Disease

“…As recommended 14,15 , both distribution-based and anchor-based techniques were performed to determine MID estimates for the SPPB subtests and summary score. Four distributionbased techniques were applied: standard error of measurement (SEM) = SD baseline * √ 1-intraclass correlation coefficient; empirical rule effect size = 0.08*6*SD delta ;…”

“…To perform anchor-based methods, at least a moderate correlation between the anchors and change in SPPB subtests or summary score (r ≥0.3, p<0.05) 14,15 was required. The chosen anchors were CAT and 6MWT 36,37 , with known MIDs and expected correlations with the change in SPPB.…”

“…Estimated minimal important differences (MIDs) are available for the SPPB subtests 4-meter gait speed (4MGS) 12 and 5-repetition sit-to-stand (5STS) in patients with COPD 13 , but are currently lacking for the SPPB summary score. This complicates the evaluation of intervention efficacy using the SPPB 14,15 . Thus, to improve the interpretation of intervention efficacy on individual and group level, the aims of the present study were: to determine the response to a PR program on the SPPB subtests and summary score in patients with COPD; and to estimate MIDs for the SPPB subtests and summary score in patients with COPD.…”

Establishing Minimal Clinically Important Differences for the Quality of Life Instrument in Patients with Esophageal Cancer QLICP-ES (V2.0) Based on Anchor-Based and Distribution-Based Methods

“…Standard Error of the Measurement (SEM), de ned as the baseline SD multiplied by the square root of one minus sample test-retest reliability coe cient, were also calculated for comparison purpose [20]. The reliability is usually estimated using a test-retest reliability estimate, but some authors also use an internal consistency estimate, for example Cronbach's alpha [21]. The calculation formula and the corresponding MCID are as follows:…”

The minimal perceived change: a formal model of the responder definition according to the patient’s meaning of change for patient-reported outcome data analysis and interpretation

“…However, it seems unreasonable to think that all the estimators proposed for determining an MID according to the patient’s subjective perspective of change are appropriate. In a recent study to determine the MID of the General Health domain of the SF-36 (a common PRO used to assess HRQoL), the resulting MID value ranged from 1 to 26 on a 100-unit scale according to the different proposed estimators [ 39 ].…”