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Purpose: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items. Methods: Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients' characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression. Results: Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status. Conclusion: PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics. Plain English summary: Patient-reported depression screening measures are prone to differential item functioning (DIF), which occurs when patients with the same levels of depression respond to and interpret the questions on the measures differently, as a result of different socio-demographic characteristics. Heterogeneity in how epilepsy patients interpret and respond to depression measures, if not identified, could lead to measurement biases that threaten the validity of inferences from patient-reported outcome measure scores to inform clinical and healthcare decisions for epilepsy management. Using data from a clinical registry of patients with epilepsy, the tree-based item response theory model was used to examine the presence of DIF in the items comprising the patient-reported Neurological Disorders Depression Inventory in Epilepsy (NDDI-E) scale. Four subgroups of epilepsy patients were noted to exhibit DIF on NDDI-E items. These groups were defined by interactions among employment status, age and sex. These characteristics affect the interpretation of NDDI-E item scores. It is recommended that clinicians carefully scrutinize responses to individual items alongside the overall NDDI-E score in different patient groups to inform clinical decisions for epilepsy management. [ABSTRACT FROM AUTHOR]

Purpose: Unsupervised item-response theory (IRT) models such as polytomous IRT based on recursive partitioning (IRTrees) and mixture IRT (MixIRT) models can be used to assess differential item functioning (DIF) in patient-reported outcome measures (PROMs) when the covariates associated with DIF are unknown a priori. This study examines the consistency of results for IRTrees and MixIRT models. Methods: Data were from 4478 individuals in the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry who received cardiac angiography in Alberta, Canada, and completed the Hospital Anxiety and Depression Scale (HADS) depression subscale items. The partial credit model (PCM) based on recursive partitioning (PCTree) and mixture PCM (MixPCM) were used to identify covariates associated with differential response patterns to HADS depression subscale items. Model covariates included demographic and clinical characteristics. Results: The median (interquartile range) age was 64.5(15.7) years, and 3522(78.5%) patients were male. The PCTree identified 4 terminal nodes (subgroups) defined by smoking status, age, and body mass index. A 3-class PCM fits the data well. The MixPCM latent classes were defined by age, disease indication, smoking status, comorbid diabetes, congestive heart failure, and chronic obstructive pulmonary disease. Conclusion: PCTree and MixPCM were not consistent in detecting covariates associated with differential interpretations of PROM items. Future research will use computer simulations to assess these models' Type I error and statistical power for identifying covariates associated with DIF. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this systematic review was to describe the prevalence and magnitude of response shift effects, for different response shift methods, populations, study designs, and patient-reported outcome measures (PROM)s. Methods: A literature search was performed in MEDLINE, PSYCINFO, CINAHL, EMBASE, Social Science Citation Index, and Dissertations & Theses Global to identify longitudinal quantitative studies that examined response shift using PROMs, published before 2021. The magnitude of each response shift effect (effect sizes, R-squared or percentage of respondents with response shift) was ascertained based on reported statistical information or as stated in the manuscript. Prevalence and magnitudes of response shift effects were summarized at two levels of analysis (study and effect levels), for recalibration and reprioritization/reconceptualization separately, and for different response shift methods, and population, study design, and PROM characteristics. Analyses were conducted twice: (a) including all studies and samples, and (b) including only unrelated studies and independent samples. Results: Of the 150 included studies, 130 (86.7%) detected response shift effects. Of the 4868 effects investigated, 793 (16.3%) revealed response shift. Effect sizes could be determined for 105 (70.0%) of the studies for a total of 1130 effects, of which 537 (47.5%) resulted in detection of response shift. Whereas effect sizes varied widely, most median recalibration effect sizes (Cohen's d) were between 0.20 and 0.30 and median reprioritization/reconceptualization effect sizes rarely exceeded 0.15, across the characteristics. Similar results were obtained from unrelated studies. Conclusion: The results draw attention to the need to focus on understanding variability in response shift results: Who experience response shifts, to what extent, and under which circumstances? [ABSTRACT FROM AUTHOR]

Purpose: Our aim is to advance response shift research by explicating the implications of published syntheses by the Response Shift – in Sync Working Group in an integrative way and suggesting ways for improving the quality of future response shift studies. Methods: Members of the Working Group further discussed the syntheses of the literature on definitions, theoretical underpinnings, operationalizations, and response shift methods. They outlined areas in need of further explication and refinement, and delineated additional implications for future research. Results: First, the proposed response shift definition was further specified and its implications for the interpretation of results explicated in relation to former, published definitions. Second, the proposed theoretical model was further explained in relation to previous theoretical models and its implications for formulating research objectives highlighted. Third, ways to explore alternative explanations per response shift method and their implications for response shift detection and explanation were delineated. The implications of the diversity of the response shift methods for response shift research were presented. Fourth, the implications of the need to enhance the quality and reporting of the response shift studies for future research were sketched. Conclusion: With our work, we intend to contribute to a common language regarding response shift definitions, theory, and methods. By elucidating some of the major implications of earlier work, we hope to advance response shift research. [ABSTRACT FROM AUTHOR]

Purpose: The Seattle Angina Questionnaire (SAQ) is a widely used patient-reported measure of health status in patients with coronary artery disease. Comparisons of SAQ scores amongst population groups and over time rely on the assumption that its factorial structure is invariant. This study evaluates the measurement invariance of the SAQ across different demographic and clinical groups and over time. Methods: Data were obtained from the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry, a registry of patients who received coronary angiogram in Alberta, Canada. The study cohort consists of adult patients who completed the paper-based version of the 16-item Canadian version of the SAQ (SAQ-CAN) 2 weeks and 1-year post-coronary angiogram between 2009 and 2016. Multi-group confirmatory factor analysis was used to assess configural, weak, strong, and strict measurement invariance across age groups, sex, angina type, treatment, and over time. Model fit was assessed using the comparative fit index and root mean square error of approximation. Results: Of the 8101 patients included in these analysis, 1300 (16.1%) were at least 75 years old, while 1755 (21.7%) were female, 5154 (63.6%) were diagnosed with acute coronary syndrome, 1177 (14.5%) received coronary artery bypass graft treatment, and 3279 had complete data on the SAQ-CAN at both occasions. There was evidence of strict invariance across age, sex, and angina type, and treatment groups, but partial strict invariance was established over time. Conclusion: SAQ-CAN can be used to compare the health status of coronary artery disease patients across population groups and over time. [ABSTRACT FROM AUTHOR]

Purpose: This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally propose advancing new methods that address the limitations of extant methods. Methods: Based on literature reviews, this critical examination comprises design-based, qualitative, individualized, and preference-based methods, latent variable models, and other statistical methods. We critically appraised their definition, operationalization, the type of response shift they can detect, whether they can adjust for and explain response shift, their assumptions, and alternative explanations. Overall limitations requiring new methods were identified. Results: We examined 11 methods that aim to operationalize response shift, by assessing change in the meaning of one's self-evaluation. Six of these methods distinguish between change in observed measurements (observed change) and change in the construct that was intended to be measured (target change). The methods use either (sub)group-based or individual-level analysis, or a combination. All methods have underlying assumptions to be met and alternative explanations for the inferred response shift effects. We highlighted the need to address the interpretation of the results as response shift and proposed advancing new methods handling individual variation in change over time and multiple time points. Conclusion: No single response shift method is optimal; each method has strengths and limitations. Additionally, extra steps need to be taken to correctly interpret the results. Advancing new methods and conducting computer simulation studies that compare methods are recommended to move response shift research forward. [ABSTRACT FROM AUTHOR]

Purpose: The Working Group undertook a critical, comprehensive synthesis of the response shift work to date. We aimed to (1) describe the rationale for this initiative; (2) outline how the Working Group operated; (3) summarize the papers that comprise this initiative; and (4) discuss the way forward. Methods: Four interdisciplinary teams, consisting of response shift experts, external experts, and new investigators, prepared papers on (1) definitions and theoretical underpinnings, (2) operationalizations and response shift methods, (3) implications for healthcare decision-making, and (4) on the published magnitudes of response shift effects. Draft documents were discussed during a two-day meeting. Papers were reviewed by all members. Results: Vanier and colleagues revised the formal definition and theory of response shift, and applied these in an amended, explanatory model of response shift. Sébille and colleagues conducted a critical examination of eleven response shift methods and concluded that for each method extra steps are required to make the response shift interpretation plausible. Sawatzky and colleagues created a framework for considering the impact of response shift on healthcare decision-making at the level of the individual patient (micro), the organization (meso), and policy (macro). Sajobi and colleagues are conducting a meta-analysis of published response shift effects. Preliminary findings indicate that the mean effect sizes are often small and variable across studies that measure different outcomes and use different methods. Conclusion: Future response shift research will benefit from collaboration among diverse people, formulating alternative hypotheses of response shift, and conducting the most conclusive studies aimed at testing these (falsification). [ABSTRACT FROM AUTHOR]