How strong should my anchor be for estimating group and individual level meaningful change? A simulation study assessing anchor correlation strength and the impact of sample size, distribution of change scores and methodology on establishing a true meaningful change threshold

Psychometric evaluation of the near activity visual questionnaire presbyopia (NAVQ-P) and additional patient-reported outcome items

BACKGROUND

The Near Visual Acuity Questionnaire Presbyopia (NAVQ-P) is a patient-reported outcome (PRO) measure that was developed in a phakic presbyopia population to assess near vision function impacts. The study refined and explored the psychometric properties and score interpretability of the NAVQ-P and additional PRO items assessing near vision correction independence (NVCI), near vision satisfaction (NVS), and near vision correction preference (NVCP).

METHODS

This was a psychometric validation study conducted using PRO data collected as part of a Phase IIb clinical trial (CUN8R44 A2202) consisting of 235 randomized adults with presbyopia from the US, Japan, Australia, and Canada. Data collected at baseline, week 2, and months 1, 2, and 3 during the 3-month trial treatment period were included in the analyses to assess item (question) properties, NAVQ-P dimensionality and scoring, reliability, validity, and score interpretation.

RESULTS

Item responses were distributed across the full response scale for most NAVQ-P and additional PRO items. Confirmatory factor analysis supported the pre-defined unidimensional structure and calculation of a NAVQ-P total score as a measure of near vision function. Item deletion informed by item response distributions, dimensionality analyses, item response theory, and previous qualitative findings, including clinical input, supported retention of 14 NAVQ-P items. The 14-item NAVQ-P total score had excellent internal consistency (α = 0.979) and high test-retest reliability (Intraclass Correlation Coefficients > = 0.898). There was good evidence of construct-related validity for all PROs supported by strong correlations with concurrent measures. Excellent results for known-groups validity and ability to detect change analyses were also demonstrated. Anchor-based and distribution-based methods supported interpretation of scores through generation of group-level and within-individual estimates of meaningful change thresholds. A meaningful within-patient change in the range of 8-15-point improvement on the NAVQ-P total score (score range 0-42) was recommended, including a more specific responder definition of 10-point improvement.

CONCLUSIONS

The NAVQ-P, NVCI, and NVS are valid and reliable instruments which have the ability to detect change over time. Findings strongly support the use of these measures as outcome assessments in clinical/research studies and in clinical practice in the presbyopia population.

A Review of Meaningful Change Thresholds for EORTC QLQ-C30 and FACT-G Within Oncology

OBJECTIVES

This literature review provides an overview of meaningful change thresholds for the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (QLQ-C30) and the Functional Assessment of Cancer Therapy - General (FACT-G) used across hematological cancers and solid tumors (melanoma, lung, bladder, and prostate).

METHODS

Embase, MEDLINE, and PubMed were searched to identify relevant oncology publications from 2016 to 2021. Label claims from the US Food and Drug Administration and the European Medicines Agency for 7 recently approved drugs (pembrolizumab, atezolizumab, glasdegib, gilteritinib, tisagenlecleucel, axicabtagene ciloleucel, and daratumumab plus hyaluronidase-fihj) were reviewed.

RESULTS

Publications providing guidance on meaningful change thresholds for the QLQ-C30 displayed a growing trend away from broad "legacy" thresholds of 10 points for all QLQ-C30 scales), toward deriving "contemporary" thresholds (eg, subscale specific, population specific). Contemporary publications generally provide guidance on selecting thresholds for specific scales that account for improved or worsening thresholds (eg, QLQ-C30 subscales). This trend was not clear for FACT-G, with less new guidance available. Most clinical trials used in regulatory label submissions have used thresholds of 10 points for the QLQ-C30 subscales and 3 to 7 points for the FACT-G total score. Despite the availability of more recent guidelines, contemporary meaningful change thresholds seem slow to emerge in the published literature and regulatory labels.

CONCLUSIONS

Trialists should consider using contemporary thresholds, rather than legacy thresholds, for QLQ-C30 endpoints. Thresholds derived for a similar patient-population should be used where available. Further work is required to provide these across a broader range of cancer sites.

Meaningful within-patient change for clinical outcome assessments: model-based approach versus cumulative distribution functions

OBJECTIVES

The FDA recommends the use of anchor-based methods and empirical cumulative distribution function (eCDF) curves to establish a meaningful within-patient change (MWPC) for a clinical outcome assessment (COA). In practice, the estimates obtained from model-based methods and eCDF curves may not closely align, although an anchor is used with both. To help interpret their results, we investigated and compared these approaches.

METHODS

Both repeated measures model (RMM) and eCDF approaches were used to estimate an MWPC on a target COA. We used both real-life (ClinicalTrials.gov: NCT02697773) and simulated data sets that included 688 patients with up to six visits per patient, target COA (range 0 to 10), and an anchor measure on patient global assessment of osteoarthritis from 1 (very good) to 5 (very poor). Ninety-five percent confidence intervals for the MWPC were calculated by the bootstrap method.

RESULTS

The distribution of the COA score changes affected the degree of concordance between RMM and eCDF estimates. The COA score changes from simulated normally distributed data led to greater concordance between the two approaches than did COA score changes from the actual clinical data. The confidence intervals of MWPC estimate based on eCDF methods were much wider than that by RMM methods, and the point estimate of eCDF methods varied noticeably across visits.

CONCLUSIONS

Our data explored the differences of model-based methods over eCDF approaches, finding that the former integrates more information across a diverse range of COA and anchor scores and provides more precise estimates for the MWPC.