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Purpose Estimates of the minimally important change (MIC) can be used to evaluate whether group-level differences are large enough to be important. But responders to treatment have been based upon group-level MIC thresholds, resulting in inaccurate classification of change over time. This article reviews options and provides suggestions about individual-level statistics to assess whether individuals have improved, stayed the same, or declined. Methods Review of MIC estimation and an example of misapplication of MIC group-level estimates to assess individual change. Secondary data analysis to show how perceptions about meaningful change can be used along with significance of individual change. Results MIC thresholds yield over-optimistic conclusions about responders to treatment because they classify those who have not changed as responders. Conclusions Future studies need to evaluate the significance of individual change using appropriate individual-level statistics such as the reliable change index or the equivalent coefficient of repeatability. Supplementing individual statistical significance with retrospective assessments of change is desirable.

We define the minimal important change (MIC) as a threshold for a minimal within-person change over time above which patients perceive themselves importantly changed. There is a lot of confusion about the concept of MIC, particularly about the concepts of minimal important change and minimal detectable change, which questions the validity of published MIC values. The aims of this study were: (1) to clarify the concept of MIC and how to use it; (2) to provide practical guidance for estimating methodologically sound MIC values; and (3) to improve the applicability of PROMIS by summarizing the available evidence on plausible PROMIS MIC values. We discuss the concept of MIC and how to use it and provide practical guidance for estimating MIC values. In addition, we performed a systematic review in PubMed on MIC values of any PROMIS measure from studies using recommended approaches. A total of 50 studies estimated the MIC of a PROMIS measure, of which 19 studies used less appropriate methods. MIC values of the remaining 31 studies ranged from 0.1 to 12.7 T-score points. We recommend to use the predictive modeling method, possibly supplemented with the vignette-based method, in future MIC studies. We consider a MIC value of 2–6 T-score points for PROMIS measures reasonable to assume at this point. For surgical interventions a higher MIC value might be appropriate. We recommend more high-quality studies estimating MIC values for PROMIS.

Poor medication adherence is associated with reduced drug effectiveness, poor health-related quality of life, increased morbidity and mortality, and increased healthcare utilization and cost. Including the patient’s voice is essential in understanding barriers to adherence. Useful patient-reported adherence measures are brief, inexpensive, non-invasive; can indicate barriers to adherence; and can be incorporated in electronic health records. The NIH Patient-Reported Outcomes Measurement Information System (PROMIS®) includes high-quality, freely available patient-reported measures covering many important constructs in patient-centered research but does not include a medication adherence measure. To fill this gap, we developed the PROMIS Medication Adherence Scale (PMAS) using the rigorous PROMIS instrument development guidelines. To develop the PMAS, we first conducted a review of the reviews, which enabled us to identify content areas relevant to medication adherence behavior. Then, we conducted qualitative research to elicit patients’ views of and experiences with medication adherence. This process identified the following important content areas to guide item writing: extent medication is taken, knowledge of medication regimen, beliefs about medication, remembering to take medication, skipping due to side effects, skipping due to feeling better, and cost of medications. Based on the results of these activities, we wrote items and aimed to retain 1– 2 items per content area. The final item set included 9 total adherence items, which were then refined through intensive comprehension and translatability review, as well as cognitive interviews. Future steps include testing the PMAS’s validity.

Patient-reported outcomes assessing treatment toxicity and its impact, and aspects of quality of life, have become increasingly important components of oncology drug development including early phase dose optimization studies. Despite insights from these data, there are limitations on drawing inferences about whether a treatment is tolerable or intolerable, which requires understanding individual risk-benefit assessments that vary between patients, cancer types, and stages of disease. This commentary examines unresolved methodological issues requiring more research rigor and attention: the development of tolerability thresholds based upon toxicity severity data; the direct measurement of tolerability through specific patient self-report measures; and overcoming the challenge of baseline value assessment in important measures such as single item side-effect bother assessments. We call for further research to resolve these challenges and drive more robust inference-making from patient-reported outcomes data collected in cancer clinical trials. Improving How We Measure Treatment Tolerability in Cancer Trials: When new cancer treatments are being tested, researchers need to understand not just whether they work (e.g., do they remove tumors or reduce their size), but also whether patients can tolerate them. Current clinical trials use questionnaires to ask patients about the severity of treatment side effects and how much they impact daily activities. This is valuable to understand treatment toxicity and to enable comparison of the side effect profiles of different drugs. But, this doesn’t tell us whether patients are able to tolerate these side effects given the potential benefits of treatment. This commentary argues that we need better methodologies to translate the reported severity of side effects to an understanding of tolerability, by developing threshold values for severity scores to define tolerable and intolerable treatment, based on patient insights. We also argue that it is important to explore direct measures of tolerability, perhaps by asking patients if they would be willing to continue treatment. Additionally, some measurement issues need to be resolved, such as asking about side effects before treatment has begun. Addressing these methodological gaps will ensure that we can derive accurate findings based on the important patient-reported outcomes data collected throughout cancer drug development.

Objective The Patient-Reported Outcomes Measurement Information System (PROMIS) aims to address the lack of generalizable and universal measure of patient-reported outcomes to assess health-related quality of life. It has not been validated for patients with chronic kidney disease. We aim to validate the PROMIS-57 and PROMIS-29 questionnaires among kidney transplant recipients. Methods A cross-sectional sample of stable kidney transplant recipients was recruited. Each participant completed PROMIS-57, a 57-question instrument covering seven domains—physical function, anxiety, depression, fatigue, pain, sleep disturbance, and social functioning—alongside validated legacy questionnaires [Patient Health Questionnaire (PHQ9), General Anxiety Disorder (GAD7), Edmonton Symptom Assessment Scale revised (ESASr), and Kidney Disease Quality of Life (KDQoL-36)]. PROMIS-29, a 29-question instrument, is nested within PROMIS-57 and measures the same domains. Structural validity of PROMIS was assessed with confirmatory factor analysis, reported using the Comparative Fit Index (CFI). Construct validity was assessed with known-groups comparisons. Internal consistency was evaluated with Cronbach’s α and convergent validity was assessed with Spearman’s Rho. Test–retest reliability was assessed through the intraclass correlation coefficient (ICC). Results Mean (± SD) age of the 177 participants was 50 (± 17), 57% were male and 55% Caucasian. Internal consistency of each domain was high (Cronbach’s α > 0.88). Confirmatory factor analysis showed good structural validity for most domains (CFI > 0.95, RMSEA < 0.05). Test–retest reliability indicated good agreement (ICC > 0.6). Known-groups comparisons by clinical and socio-demographic differences were found as hypothesized. Conclusions Our results provide evidence that PROMIS-57 and PROMIS-29 are highly reliable and valid instruments among kidney transplant recipients. We propose it as a valuable tool to assess important domains of the illness experience.

To develop a patient preference questionnaire (PPQ) assessing eculizumab and ravulizumab treatment for paroxysmal nocturnal hemoglobinuria (PNH). The development of the PNH-PPQ was consistent with Food and Drug Administration guidelines for patient-reported outcome measure development, and included 1) a targeted literature review; 2) PNH expert clinician input on treatment preferences; 3) review of existing qualitative data on the PNH treatment and disease experience; 4) concept elicitation interviews with 8 PNH patients who received eculizumab and/or ravulizumab; 5) translatability review; and 6) cognitive debriefing with 5 patients. Interview participants were recruited through a United Kingdom PNH patient advocacy group and a Canadian clinical site involved in clinical trial ALXN1210-PNH-302. Six themes were identified as most relevant to the PNH treatment experience from the concept elicitation interviews: disease symptoms (n=8/8); treatment frequency (n=7/8); quality of life impact of treatment/disease (n=7/8); treatment burden (n=7/8); treatment efficacy (n=5/8); and treatment side effects (n=5/8). An initial list of 88 preference questions was reduced to 11 highly relevant and non-redundant questions reflecting the 6 themes. Cognitive interview participants unanimously agreed that the PNH-PPQ instructions were clear; response options were understandable, easy to use, and provided enough choices; and the questions captured the factors that inform treatment preferences. When new drugs have similar efficacy to existing medications, documenting patient preferences is important for confirming patient benefit from the new medication. Understanding what matters most to patients is essential for delivering patient-centered care and may play a particularly significant role in treatment decision making. The availability of such a tool may be especially important as new orphan drugs are developed and patients with rare diseases have more than one treatment option to consider. The PNH-PPQ provides a patient-centered approach for evaluating preferences for the treatment of PNH. The PNH-PPQ has subsequently assessed patient preference in the clinical trial sub-study ALXN1210-PNH-302s.

Background As patient-reported outcome measures (PROMs) become available to clinicians for routine clinical decision-making, many wonder how to define a meaningful change in a patient’s PROM score. Some PROMs have a specific threshold that indicates meaningful change, but since those numbers are based on population averages, they do not necessarily apply to the varying experiences of each individual patient. Rather than viewing this as a weakness of PROMs, it is worth considering how clinicians use other existing measures in clinical decision-making—and whether PROMs can be used similarly. Body An informal survey of 43 clinicians reported using measures such as weight, blood pressure, and blood chemistry to inform clinical decision-making. Although clinicians were very consistent with what constituted a meaningful change for some measures (e.g., ECOG performance status), other measures had considerable variability (e.g., weight), often informed by their specialization (for example, differing thresholds for meaningful weight change for adult primary care, pediatrics, and oncology). For interpreting change in measures, they relied on clinical experience (44%), published literature (38%), and established guidelines (35%). In open-response comments, many clarified that the results of any measure had to be taken in the context of each individual patient before making treatment decisions. In short, clinicians already apply individualized clinical judgment when interpreting score changes in existing clinical measures. As clinicians gain familiarity with PROMs, PROMs will likely be utilized in the same way. Conclusion Like other clinical measures from weight to blood chemistry, change in a PROM score is but one piece of a patient’s clinical story. Rather than relying on a hard-and-fast number for defining clinically meaningful change in a PROM score, providers should—and many already do—consider the full scope of a patient’s experience as they make treatment decisions.

An application of telemedicine of growing interest and relevance is the use of personal computers and mobile devices to collect patient-reported outcomes (PROs). PROs are self-reports of patients’ health status without interpretation by anyone else. The tools developed to assess PROs are known as patient-reported outcomes measures (PROMs). The technological innovations that have led to an increased ownership of electronic devices have also facilitated the development of electronic PROMs (ePROMs). ePROMs are a conduit for telemedicine in the care of patients with chronic diseases. Various studies have demonstrated that the use of ePROMs in routine clinical practice is both acceptable and feasible with patients increasingly expressing a preference for an electronic mode of administration. There is increasing evidence that the use of electronic patient-reported outcome (ePROMs) could have significant impacts on outcomes valued by patients, healthcare providers and researchers. Whilst the development and implementation of these systems may be initially costly and resource-intensive, patient preferences and existing evidence to support their implementation suggests the need for continued research prioritisation in this area. This narrative review summarises and discusses evidence of the impact of ePROMs on clinical parameters and outcomes relevant to chronic diseases. We also explore recently published literature regarding issues that may influence the robust implementation of ePROMs for routine clinical practice.

Background Aromatic L-amino acid decarboxylase deficiency (AADCd) is a rare genetic disorder characterized by movement disorders, motor and autonomic dysfunction, and developmental delays. The gene therapy eladocagene exuparvovec has become available in some regions; pooled clinical trial results demonstrate continuous long-term improvement in motor development and cognitive function. We sought to characterize clinically meaningful change in motor function, as measured by Total Peabody Developmental Motor Scales-Second Edition (PDMS-2) score, and assess correlations with cognition and language domains of the Bayley-III and motor milestone (MM) achievement. Methods Data from N  = 30 patients from three single-arm clinical studies of eladocagene exuparvovec were analyzed. Anchor-based estimation of the meaningful score difference (MSD) of Total PDMS-2 score was conducted using mean-difference and receiver operating characteristic curve (ROC) approaches. MM achievement served as the anchor defining meaningful change. Results An MSD of 40 points for Total PDMS-2 score was selected for analysis as it yielded specificity > 0.95 using the ROC approach, and generally aligned with the mean-difference approach. Cumulative incidence analysis reflected that 50% of patients treated with eladocagene exuparvovec may achieve the MSD of 40-point change in Total PDMS-2 score at 6 months, and 86% at 18 months. Correlations between measures were of large magnitude and improved over time (Month 6: r  = 0.599 [ p  = 0.0032]; Month 18: r  = 0.796 [ p  = 0.0002]; Month 60: r  = 0.861 [ p  = 0.0007]). Conclusions The MSD of 40 points for Total PDMS-2 score enables the interpretation of changes observed in patients with AADCd, and suggests that treatment with eladocagene exuparvovec leads to significant improvements in motor and cognitive function.