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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.

Purpose Clinical outcome assessments (COAs) require evidence not only of reliability, validity, and ability to detect change, but also a definition of what constitutes a meaningful change on the instrument. The responder definition specifies the amount of change on the COA that may be interpreted as a treatment benefit and is critical for interpreting what constitutes a meaningful change on the COA scores. However, the literature that describes methods for developing and applying responder definitions can be difficult to navigate. Clear and concise guidelines regarding which methods to apply under what circumstances and how to interpret the results are lacking. This article provides a guide to the variety of available methods and issues that should be considered when establishing responder definitions for interpreting meaningful changes in COA scores. Methods An overview is provided for selecting anchors, developing study designs, planning psychometric analyses, using psychometric results to set responder thresholds, and applying responder thresholds in demonstrating treatment efficacy. Results There are a variety of anchor-based methods for consideration, but they all rely on a preference for strongly related and easily interprétable anchors. The benefits of applying multiple anchors and multiple analytic methods are discussed. The process of triangulation can synthesize results across multiple sources to gain confidence in a proposed responder definition. Though a link to meaningfulness from the patient's perspective is absent, distribution-based methods provide lower bound estimates of score precision and have a role in triangulation. Responder definitions are typically required within regulatory review, but their application may differ across clinical trial programs. Conclusions By careful planning of anchor selection, study design, and psychometric methods, COA researchers can establish defensible responder thresholds that ultimately aid patients and clinicians in making informed treatment decisions.

BACKGROUND:Patient-reported outcome (PRO) data may be used at 2 levelsto evaluate impacts of disease and treatment aggregated across individuals (group-level) and to screen/monitor individual patients to inform their management (individual-level). For PRO data to be useful at either level, we need to understand their clinical relevance. PURPOSE:To provide clarity on whether and how methods historically developed to interpret group-based PRO research results might be applied in clinical settings to enable PRO data from individual patients to inform their clinical management and decision-making. METHODS:We first differentiate PRO-based decision-making required at group versus individual levels. We then summarize established group-based approaches to interpretation (anchor-based and distribution based), and more recent methods that draw on item calibrations and qualitative research methods. We then assess the applicability of these methods to individual patient data and individual-level decision-making. FINDINGS:Group-based methods provide a range of thresholds that are useful in clinical caresome provide screening thresholds for patients who need additional clinical assessment and/or intervention, some provide thresholds for classifying an individual’s level of severity of symptoms or problems with function, and others provide thresholds for meaningful change when monitoring symptoms and functioning over time during or after interventions. Availability of established cut-points for screening and symptom severity, and normative/reference values, may play into choice of PRO measures for use in clinical care. Translatability of thresholds for meaningful change is more problematic because of the greater reliability needed at the individual-level versus group-level, but group-based methods may provide lower bound estimates. Caution is needed to set thresholds above bounds of measurement error to avoid “false-positive changes” triggering unwarranted alerts and action in clinic. CONCLUSIONS:While there are some challenges in applying available methods for interpreting group-based PRO results to individual patient data and clinical care—including myriad contextual factors that may influence an individual patient’s management and decision-making—they provide a useful starting point, and should be used pragmatically.

Purpose The notion of what constitutes meaningful differences or changes in patient-reported outcome scores is represented by meaningful change thresholds (MCTs). Applying multiple methods to estimate MCTs inevitably results in a range of estimates; however, a single estimate or small range is sought in practice to enable consistent interpretation of scores. While current recommendations for triangulation are appropriate in principle, the vital step of moving from all estimates to a value or small range lacks clarity and is subjective in nature. This article aims to review current triangulation approaches and provide more robust recommendations than what is currently available. Methods Current approaches to perform triangulation are described and discussed. Anchor-based estimates are focussed upon due to their recognition as the most valid and developed approach. Recommendations for triangulation are provided. Results A correlation-weighted average of MCT estimates is recommended to triangulate multiple MCT estimates derived from a single study into a single value, where increased weighting is given to stronger anchor measures. The choice of method to triangulate estimates from several published studies is highly dependent on the availability of information within the publications. MCTs designed for between-group differences, within-group changes, and within-individual changes should be considered separately. Conclusion The recommendations within this article provide a reliable and transparent approach to triangulation when a single value is sought, based on meta-analytic approaches. This approach is preferable to a simple mean of estimates where all are weighted equally, or through ‘eyeballing’ plotted estimates which is unreliable. We encourage researchers to adopt these methods, but to remain aware of the limitations within each method and further nuances in study design that result in heterogeneity. Sensitivity analyses with a range of plausible values are encouraged; however, the recommendations provide a suitable starting value for inferences. Unresolved issues in triangulation, requiring further exploration, are highlighted.

Purpose The recommended method for establishing a meaningful threshold for individual changes in patient-reported outcome (PRO) scores over time uses an anchor-based method. The patients assess their perceived level of change and this is used to define a threshold on the PRO score which may be considered meaningful to the patient. In practice, such an anchor may not be available. In the absence of alternative information often the meaningful change threshold for assessing between-group differences, the minimally important difference, is used to define meaningful change at the individual level too. This paper will highlight the issues with this, especially where the underlying measurement scale is not continuous. Methods Using the EORTC QLQ-C30 as an example, plausible score increments (“state changes”) are calculated for each subscale highlighting why commonly used thresholds may be misleading, including leading to sensitivity analyses that are inadvertently testing the same underlying threshold. Results The minimal possible individual score change varies across subscales; 6.7 for Physical Functioning, 8.3 for Global Health Scale and Emotional Functioning, 11.1 for fatigue, 16.7 for role functioning, cognitive functioning, social functioning, nausea and vomiting, pain and 33.3 for single items. Conclusions The determination of meaningful change for an individual patient requires input from the patients but being mindful of the underlying scale ensures that these thresholds are also guided by what is a plausible change for patients to achieve on the scale.

Purpose Interpretation guidelines are needed for patient-reported outcome (PRO) measures' change scores to evaluate efficacy of an intervention and to communicate PRO results to regulators, patients, physicians, and providers. The 2009 Food and Drug Administration (FDA) Guidance for Industry Patient-Reported Outcomes (PRO) Measures: Use in Medical Product Development to Support Labeling Claims (hereafter referred to as the final FDA PRO Guidance) provides some recommendations for the interpretation of change in PRO scores as evidence of treatment efficacy. Methods This article reviews the evolution of the methods and the terminology used to describe and aid in the communication of meaningful PRO change score thresholds. Results Anchor- and distribution-based methods have played important roles, and the FDA has recently stressed the importance of cross-sectional patient global assessments of concept as anchor-based methods for estimation of the responder definition, which describes an individual-level treatment benefit. The final FDA PRO Guidance proposes the cumulative distribution function (CDF) of responses as a useful method to depict the effect of treatments across the study population. Conclusions While CDFs serve an important role, they should not be a replacement for the careful investigation of a PRO's relevant responder definition using anchor-based methods and providing stakeholders with a relevant threshold for the interpretation of change over time.

ObjectiveHereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN) is a rare disease characterized by rapid neuropathic progression. In pivotal studies of gene-silencing treatments, the modified Neuropathy Impairment Score + 7 tests (mNIS + 7) and Norfolk-Quality of Life (QOL)-Diabetic Neuropathy (DN) questionnaire assessed treatment impact on neuropathic progression. Establishing responder definition (RD) thresholds for these measures would enable evaluation of clinically meaningful treatment benefit.MethodsmNIS + 7 and Norfolk-QOL-DN were administered at baseline and week 65 to 165 adults with ATTRv-PN receiving inotersen (n = 106) or placebo (n = 59) in the NEURO-TTR study. Anchor-based approaches for estimating RD thresholds were used for Norfolk QOL-DN, while distribution-based approaches were used for both measures. Responders were patients with a score change < RD, indicating improvement or stabilization (i.e., no clinically meaningful progression). Odds ratios (ORs) and Fisher’s exact tests compared proportions of responders by treatment.ResultsThe mean RD estimates were 12.2 points and 8.8 points for mNIS + 7 and Norfolk QOL-DN, respectively. The proportions of patients whose change in score indicated improvement or stabilization were statistically significantly larger for inotersen than placebo for all estimated RD thresholds for mNIS + 7 (64–86% responders for inotersen vs. 27–46% for placebo, ORs = 3.8–7.2, ps < 0.001) and Norfolk QOL-DN (66–81% vs. 35–56%, ORs = 2.4–3.6, ps < 0.05).DiscussionEstablishing RD thresholds for these instruments enables evaluation of clinically relevant and individual-level treatment benefit on neuropathic progression. Across RDs estimated using multiple methods, a higher proportion of patients receiving inotersen than placebo showed improved or stabilized neuropathic progression at week 65.Trial registrationClinicalTrials.gov Identifier: NCT01737398; Date of registration: November 29, 2012.

Purpose To compare the performance of anchor-based methods for estimating thresholds of meaningful within-patient change (i.e., individual change) of clinical outcome assessments in conditions reflecting data characteristics of small- to medium-sized clinical trials. Methods Datasets were generated from the joint distributions of the PROMIS PF 20a T-score changes and a seven-point global change anchor measure. The 108 simulation conditions (1000 replications per condition) included combinations of three marginal distributions of T-score changes, three improvement percentages in the anchor measure, four levels of responsiveness correlations, and three sample sizes. Threshold estimation methods included mean change, median change, ROC curve, predictive modeling, half SD, and SEM. Relative bias, precision, accuracy, and measurement significance of the estimates were evaluated based on comparison with true thresholds and IRT-based individual reliable changes of PROMIS scores. Quantile regression models were applied to select and interpret effects of simulation conditions on estimation bias. Results When PROMIS T-score changes were distributed normally, the predictive modeling method performed best with 50% or more responders identified by the anchor; the mean and median methods were preferred with 30% responders. For skewed distributions, the median method and ROC method gained more advantages. Among the evaluated study conditions, the improvement percentage condition had the most obvious effects on estimation bias. Conclusion To establish accurate and precise thresholds, clinical researchers are recommended to prioritize study designs with at least 50% anchor-defined responders and strongly responsive target endpoints with highly reliable scoring calibration and to select optimal anchor-based methods given the data characteristics.

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.

Establishing meaningful change thresholds for Clinical Outcome Assessments (COA) is critical for score interpretation. While anchor- and distribution-based statistical methods are well-established, qualitative approaches are less frequently used. This commentary summarizes and expands on a symposium presented at the International Society for Quality of Life Research (ISOQOL) 2017 annual conference, which provided an overview of qualitative methods that can be used to support understanding of meaningful change thresholds on COAs. Further published literature and additional examples from multiple disease areas which have also qualitatively explored the concept of meaningful change are presented. Semi-structured interviews conducted independently from a clinical trial, exit interviews conducted in the context of a clinical trial, focus groups, vignettes and the Delphi panel method can be used to obtain data regarding meaningful change thresholds, with advantages and disadvantages to each method. Semi-structured interviews using concept elicitation (CE) or cognitive debriefing (CD) methods conducted independently from a clinical trial can be an efficient way to gain in-depth patient/caregiver insights. However, there can be challenges with reconciling heterogeneous data across diverse samples and in interpreting the qualitative insights in the context of quantitative score changes. Semi-structured qualitative interviews using CE/CD methods embedded as exit interviews in a clinical trial context with patients/caregivers can provide insights which can augment quantitative findings based on analysis of clinical trial data. However, there are logistical challenges relating to embedding the interviews in a clinical trial. Focus groups and the Delphi panel method can be valuable for reaching consensus regarding meaningful change thresholds; however, for face-to-face interactions, social desirability bias can affect responses. Finally, using vignettes and taking a mixed methods approach can aid in achieving consensus on the minimum score change endorsed by respondents as a meaningful improvement/decrement. However, the approach can be cognitively challenging for participants and reaching a consensus is not guaranteed. Anchor- and distribution- based methods remain critical in establishing responder definitions. Nonetheless, qualitative data has the potential to provide complementary support that a certain level of change on the target COA, which has been statistically supported, is truly important and meaningful for the target population.