Minimal clinically important differences in health-related quality of life after treatment with direct-acting antivirals for chronic hepatitis C: ANRS CO22 HEPATHER cohort (PROQOL-HCV)

PURPOSE

Patient Reported Outcomes Quality of Life survey for HCV (PROQOL-HCV) is a specific tool developed to assess health-related quality of life (HRQoL) in patients with chronic hepatitis C receiving direct-acting antivirals (DAA). Thresholds for clinically meaningful changes in PROQOL-HCV scores should be documented to improve the tool's use in clinical practice. This study aimed to estimate the minimal clinically important differences (MCIDs) in PROQOL-HCV scores before and after HCV cure by DAA among participants in the prospective cohort ANRS-CO22 HEPATHER.

METHODS

Data from 460 chronic HCV patients were collected at DAA initiation (baseline) and 24 weeks after treatment end. MCIDs were estimated for the six HRQoL dimensions (Physical Health (PH), Emotional Health (EH), Future Uncertainty (FU), Intimate Relationships (IR), Social Health (SH), and Cognitive Functioning (CF)) using two approaches: anchor-based and score distribution-based. Each MCID was estimated for improvement/deterioration both globally and separately for patients with a baseline PRQoL-HCV score ≤ 50 (group1) and patients with a baseline PRQoL-HCV score > 50 (group2).

RESULTS

The pooled MCIDs for improvement/deterioration globally, in group1, and in group2, respectively, were as follows: 8.8/- 7.6, 9.7/- 9.5, and 6.0/- 6.9 for PH; 7.1/- 4.6, 7.7/- 9.6, and 6.6/- 6.7 for EH; 6.7/- 6.7, 8.2/- 8.2, and 6.0/- 6.0 for FU; 7.0/- 7.0, 5.4/- 5.4, and 6.2/- 6.2 for IR; 7.7/- 7.7, 8.6/- 8.6, and 6.5/- 6.5 for SH; 7.3/- 5.6, 9.1/- 8.0, and 6.5/- 6.3 for CF.

CONCLUSIONS

The overall MCID for the PROQOL-HCV scores ranged from 6.7 to 8.8 for improvement and from - 7.7 to - 4.6 for deterioration. The effect of DAA on PROQOL-HCV scores seemed particularly beneficial for patients with lower baseline scores. This subgroup could be motivated to take DAA if they are informed of the benefits for their HRQoL.

Assessing Clinically Important Differences During Rehabilitation for Stroke: A Pilot Study Evaluating Anchor and Distribution Derived Estimates of Physical Function Change in Classically Summed and Rasch Models of Section GG of the Inpatient Rehabilitation Facility Patient Assessment Instrument

OBJECTIVE

To determine clinically important differences (CIDs) on Section GG physical functioning scores on the Centers for Medicare and Medicaid Services (CMS) Inpatient Rehabilitation Facility Assessment Instrument (IRF-PAI) for patients with stroke, using anchor and distribution-based approaches.

DESIGN

Pilot prospective observational cohort study.

SETTING

Inpatient rehabilitation facility.

PARTICIPANTS

Patients with stroke (N=208).

INTERVENTIONS

Physicians assessed improvements during rehabilitation using the modified Rankin scale (mRS). Improvements (≥1 point) on the mRS were used as the anchor for establishing CIDs.

MAIN OUTCOME MEASURES

Classically summed and Rasch transformed Section GG change scores associated with clinically important improvements on the mRS.

RESULTS

A total of 166 patients (79.8%) improved ≥1 point on the mRS. Change scores of 27, 9, and 16 on Section GG total physical functioning (self-care + mobility), self-care, and mobility/walk scales, respectively, had high sensitivity (0.82-0.85) but low specificity (0.52-0.69) in identifying patients improving on the mRS. Positive predictive values ranged from 0.87 to 0.91, and negative predictive values ranged from 0.42 to 0.52. Total physical functioning and selfcare anchor-derived change scores were similar to the reliable change index (RCI [2.77 × SEM]), calculated as 28 and 10 points, respectively, whereas anchor-derived mobility/walk scale change scores were equivalent to 1.96 × SEM. Exploratory Rasch modeling identified 3 Section GG subscales (R-Self-Care, R-Mobility, and R-Walking). Improvements on the R-Walking subscale were most correlated with mRS improvements (ρ=-0.47); however, accuracy of CID estimates was not improved.

CONCLUSIONS

Cut-off scores obtained using the mRS anchor aligned with more robust estimates of change, as estimated by distribution-based measures. While patients achieving anchor-derived cut-offs have a high probability of mRS improvement, change scores may fail to detect clinically meaningful improvements at these same thresholds. Alternative criteria for determining MCID/CIDs, should be explored. Rasch models require further validation.

How is the minimal clinically important difference established in health-related quality of life instruments? Review of anchors and methods

Background

The aim of this systematic review is to describe the different types of anchors and statistical methods used in estimating the Minimal Clinically Important Difference (MCID) for Health-Related Quality of Life (HRQoL) instruments.

Methods

PubMed and Google scholar were searched for English and French language studies published from 2010 to 2018 using selected keywords. We included original articles (reviews, meta-analysis, commentaries and research letters were not considered) that described anchors and statistical methods used to estimate the MCID in HRQoL instruments.

Results

Forty-seven papers satisfied the inclusion criteria. The MCID was estimated for 6 generic and 18 disease-specific instruments. Most studies in our review used anchor-based methods (n = 41), either alone or in combination with distribution-based methods. The most common applied anchors were non-clinical, from the viewpoint of patients. Different statistical methods for anchor-based methods were applied and the Change Difference (CD) was the most used one. Most distributional methods included 0.2 standard deviations (SD), 0.3 SD, 0.5 SD and 1 standard error of measurement (SEM). MCID values were very variable depending on methods applied, and also on clinical context of the study.

Conclusion

Multiple anchors and methods were applied in the included studies, which lead to different estimations of MCID. Using several methods enables to assess the robustness of the results. This corresponds to a sensitivity analysis of the methods. Close collaboration between statisticians and clinicians is recommended to integrate an agreement regarding the appropriate method to determine MCID for a specific context.

Defining equivalence in medical education evaluation and research: does a distribution-based approach work?

Educators often seek to demonstrate the equivalence of groups, such as whether or not students achieve comparable success regardless of the site at which they trained. A methodological consideration that is often underappreciated is how to operationalize equivalence. This study examined whether a distribution-based approach, based on effect size, can identify an appropriate equivalence threshold for medical education data. Thirty-nine individuals rated program site equivalence on a series of simulated pairwise bar graphs representing one of four measures with which they had prior experience: (1) undergraduate academic achievement, (2) a student experience survey, (3) an Objective Structured Clinical Exam global rating scale, or (4) a licensing exam. Descriptive statistics and repeated measures ANOVA examined the effects on equivalence ratings of (a) the difference between means, (b) variability in scores, and (c) which program site (the larger or smaller) scored higher. The equivalence threshold was defined as the point at which 50 % of participants rated the sites as non-equivalent. Across the four measures, the equivalence thresholds converged to average effect size of Cohen's d = 0.57 (range of 0.50-0.63). This corresponded to an average mean difference of 10 % (range of 3-13 %). These results are discussed in reference to findings from the health-related quality of life field that has demonstrated that d = 0.50 represents a consistent threshold for perceived change. This study provides preliminary empirically-based guidance for defining an equivalence threshold for researchers and evaluators conducting equivalence tests.