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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., (© 2023. The Author(s).)

Purpose: Because physical-mental comorbidity in children is relatively common, this study tested for response shift (RS) in children with chronic physical illness using a parent-reported measure of child psychopathology., Methods: Data come from Multimorbidity in Children and Youth across Life-course (MY LIFE), a prospective study of n = 263 children aged 2-16 years with physical illness in Canada. Parents provided information on child psychopathology using the Ontario Child Health Study Emotional Behavioral Scales (OCHS-EBS) at baseline and 24 months. Oort's structural equation modeling was used to test for different forms of RS in parent-reported assessments between baseline and 24 months. Model fit was evaluated using root mean square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean residual (SRMR)., Results: There were n = 215 (81.7%) children with complete data and were included in this analysis. Of these, n = 105 (48.8%) were female and the mean (SD) age was 9.4 (4.2) years. A two-factor measurement model provided good fit to the data [RMSEA (90% CI) = 0.05 (0.01, 0.10); CFI = 0.99; SRMR = 0.03]. Non-uniform recalibration RS was detected on the conduct disorder subscale of the OCHS-EBS. This RS effect had negligible impact on the longitudinal change in externalizing and internalizing disorders construct over time., Conclusions: Response shift detected on the conduct disorder subscale of the OCHS-EBS, indicated that parents of children with physical illness may recalibrate their responses on child psychopathology over 24 months. Researchers and health professionals should be aware of RS when using the OCHS-EBS to assess child psychopathology over time., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Purpose: Mixture item response theory (MixIRT) models can be used to uncover heterogeneity in responses to items that comprise patient-reported outcome measures (PROMs). This is accomplished by identifying relatively homogenous latent subgroups in heterogeneous populations. Misspecification of the number of latent subgroups may affect model accuracy. This study evaluated the impact of specifying too many latent subgroups on the accuracy of MixIRT models., Methods: Monte Carlo methods were used to assess MixIRT accuracy. Simulation conditions included number of items and latent classes, class size ratio, sample size, number of non-invariant items, and magnitude of between-class difference in item parameters. Bias and mean square error in item parameters and accuracy of latent class recovery were assessed., Results: When the number of latent classes was correctly specified, the average bias and MSE in model parameters decreased as the number of items and latent classes increased, but specification of too many latent classes resulted in modest decrease (i.e., < 10%) in the accuracy of latent class recovery., Conclusion: The accuracy of MixIRT model is largely influenced by the overspecification of the number of latent classes. Appropriate choice of goodness-of-fit measures, study design considerations, and a priori contextual understanding of the degree of sample heterogeneity can guide model selection., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Purpose: Item non-response (i.e., missing data) may mask the detection of differential item functioning (DIF) in patient-reported outcome measures or result in biased DIF estimates. Non-response can be challenging to address in ordinal data. We investigated an unsupervised machine-learning method for ordinal item-level imputation and compared it with commonly-used item non-response methods when testing for DIF., Methods: Computer simulation and real-world data were used to assess several item non-response methods using the item response theory likelihood ratio test for DIF. The methods included: (a) list-wise deletion (LD), (b) half-mean imputation (HMI), (c) full information maximum likelihood (FIML), and (d) non-negative matrix factorization (NNMF), which adopts a machine-learning approach to impute missing values. Control of Type I error rates were evaluated using a liberal robustness criterion for α = 0.05 (i.e., 0.025-0.075). Statistical power was assessed with and without adoption of an item non-response method; differences > 10% were considered substantial., Results: Type I error rates for detecting DIF using LD, FIML and NNMF methods were controlled within the bounds of the robustness criterion for > 95% of simulation conditions, although the NNMF occasionally resulted in inflated rates. The HMI method always resulted in inflated error rates with 50% missing data. Differences in power to detect moderate DIF effects for LD, FIML and NNMF methods were substantial with 50% missing data and otherwise insubstantial., Conclusion: The NNMF method demonstrated comparable performance to commonly-used non-response methods. This computationally-efficient method represents a promising approach to address item-level non-response when testing for DIF., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two or more populations in multivariate repeated measures designs. However, these procedures rely on the assumptions of multivariate normality which is not tenable in multivariate repeated measures designs which are characterized by binary, ordinal, or mixed types of response distributions. This study investigates the accuracy of repeated measures discriminant analysis (RMDA) based on the multivariate generalized estimating equations (GEE) framework for classification in multivariate repeated measures designs with the same or different types of responses repeatedly measured over time. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on GEE, and RMDA based on maximum likelihood estimators (MLE) under diverse simulation conditions, which included number of repeated measure occasions, number of responses, sample size, correlation structures, and type of response distribution. RMDA based on GEE exhibited higher average classification accuracy than RMDA based on MLE especially in multivariate non-normal distributions. Three repeatedly measured responses namely severity of epilepsy, current number of anti-epileptic drugs, and parent-reported quality of life in children with epilepsy were used to demonstrate the application of these procedures.

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., (© 2021. The Author(s).)

Objectives: Patient-reported outcome measures (PROMs) are used widely to elicit patient's self-appraisal of their health status and quality of life. One fundamental assumption when measuring PROMs is that all individuals interpret questions about their health status in a consistent manner. However, subgroups of patients with a similar health condition may respond differently to PROM questions (ie, differential item functioning [DIF]), leading to biased estimates of group differences on PROM scores. Understanding these differences can help inform the clinical interpretation of PROMs. This study examined whether DIF affects 10-item Quality of Life in Epilepsy (QOLIE10) scores reported by patients with epilepsy in outpatient clinics., Methods: Data were from the Calgary Comprehensive Epilepsy Program, a prospective registry of patients with epilepsy in Calgary, Alberta. Latent variable mixture models (LVMMs) based on standard two-parameter graded response models with increasing numbers of latent classes were applied to QOLIE10 item data. Model fit was assessed using the Bayesian Information Criterion (BIC) and latent class model entropy. Ordinal logistic regression was used to identify QOLIE10 items that exhibited DIF., Results: In this cohort of 1143 patients, 567 (49.6%) were female and the median age was 37.0 (interquartile range [IQR] 27.0) years. A two-class LVMM, which provided the best fit to the data, identified two subgroups of patients with different response patterns to QOLIE10 items, with class proportions of 0.62 and 0.38. The two subgroups differed with respect to antiseizure polytherapy, reported medication side effects, frequency of seizures, and psychiatric comorbidities. QOLIE10 items on the physical and psychological side effects of medication exhibited large DIF effects., Significance: Our study revealed two different response patterns to quality-of-life instruments, suggesting heterogeneity in how patients interpret some of the questions. Researchers and users of PROMs in epilepsy need to consider the differential interpretation of items for various instruments to ensure valid understanding and comparisons of PROM scores., (© 2021 International League Against Epilepsy.)

Background: Joint replacement, an increasingly common procedure amongst older adults, can substantially improve health-related quality of life (HRQoL). However, differential item functioning (DIF) may affect the accurate interpretation of differences in HRQoL amongst patients with different demographic and health status characteristics but the same underlying (i.e., latent) level of the investigated construct. This study tested for DIF in pre-operative SF-12 physical health (PH) and mental health (MH) sub-scale items amongst patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA)., Methods: Data were from a population-based joint replacement registry from the Canadian province of Manitoba. TKA and THA patients who had surgery between 2009 and 2015 and completed a pre-operative assessment were included. DIF was tested using the multiple indicators multiple causes (MIMIC) method with sex, age group, body weight status, and presence of multiple comorbid conditions (i.e., multimorbidity) as covariates. Analyses were stratified by joint type., Results: The study cohort included 8820 patients; 42.1% underwent THA, 57.3% were female, 32.7% were 70+ years, and 52.8% were obese. For each sub-scale, four of the six items exhibited DIF in both THA and TKA groups. Differences in the covariate effect estimates for DIF and No-DIF models on the MH latent variable were largest for age and body weight status for the THA group, and for sex and multimorbidity for the TKA group. All of the differences were small for PH. Multimorbidity had the strongest association with PH and age and sex had the strongest association with MH in the DIF models., Conclusions: Demographic and health status characteristics influenced SF-12 PH and MH item responses in joint replacement populations, although the size of the effects were not large for PH. We recommend testing and adjusting for DIF effects to ensure comparability of HRQoL measures in joint replacement populations.

Purpose: Perceived social support is known to be an important predictor of health outcomes in patients with acute coronary syndrome (ACS). This study investigates patterns of longitudinal trajectories of patient-reported perceived social support in individuals with ACS., Methods: Data are from 3013 patients from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry who had their first cardiac catheterization between 2004 and 2011. Perceived social support was assessed using the 19-item Medical Outcomes Study Social Support Survey (MOS) 2 weeks, 1 year, and 3 years post catheterization. Group-based trajectory analysis based on longitudinal multiple imputation model was used to identify distinct subgroups of trajectories of perceived social support over a 3-year follow-up period., Results: Three distinct social support trajectory subgroups were identified, namely: "High" social support group (60%), "Intermediate" social support group (30%), and "Low" social support subgroup (10%). Being female (OR = 1.67; 95% CI = [1.18-2.36]), depression (OR = 8.10; 95% CI = [4.27-15.36]) and smoking (OR = 1.70; 95% CI = [1.23-2.35]) were predictors of the differences among these trajectory subgroups., Conclusion: Although the majority of ACS patients showed increased or fairly stable trajectories of social support, about 10% of the cohort reported declining social support. These findings can inform targeted psycho-social interventions to improve their perceived social support and health outcomes.

Purpose: This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) patient-reported outcomes (PROs); we included methods for longitudinal latent variable measurement and growth models and response shift., Methods: Scopus, PsycINFO, PubMed, EMBASE, and Social Science Citation Index were searched for English-language studies published between 1999 and 2016 using selected keywords. Extracted information included characteristics of the study purpose/objectives, simulation design, software, execution, performance, and results. The quality of reporting was evaluated using published best-practice guidelines., Synthesis: A total of 1470 articles were reviewed and 42 articles met the inclusion criteria. The majority of the included studies (73.8%) investigated an existing statistical method, primarily a latent variable model (95.2%). Most studies specified the population model, including variable distributions, mean parameters, and correlation/covariances. The number of time points and sample size(s) were reported by all studies, but justification for the selected values was rarely provided. The majority of the studies (52.4%) did not report on model non-convergence. Bias, accuracy, and model fit were commonly reported performance metrics. All studies reported results descriptively, and 26.2% also used an inferential method., Conclusions: While methodological research on statistical analyses of complex longitudinal PRO data is informed by computer simulation studies, current reporting practices of these studies have not been consistent with best-practice guidelines. Comprehensive reporting of simulation methods and results ensures that the strengths and limitations of the investigated statistical methods are thoroughly explored.

Background: Response shift (RS) has been defined as a change in the meaning of an individual's self-evaluation of his/her health status and quality of life. Several statistical model- and design-based methods have been developed to test for RS in longitudinal data. We reviewed the uptake of these methods in patient-reported outcomes (PRO) literature., Methods: CINHAHL, EMBASE, Medline, ProQuest, PsycINFO, and Web of Science were searched to identify English-language articles about RS published until 2016. Data on year and country of publication, PRO measure adopted, RS detection method, type of RS detected, and testing of underlying model assumptions were extracted from the included articles., Results: Of the 1032 articles identified, 101 (9.8%) articles were included in the study. While 54.5 of the articles reported on the Then-test, 30.7% of the articles reported on Oort's or Schmitt's structural equation modeling (SEM) procedure. Newer RS detection methods, such as relative importance analysis and random forest regression, have been used less frequently. Less than 25% reported on testing the assumptions underlying the adopted RS detection method(s)., Conclusions: Despite rapid methodological advancements in RS research, this review highlights the need for further research about RS detection methods for complex longitudinal data and standardized reporting guidelines.

Background: It is unclear whether anxiety and depression impact health-related quality of life (HRQoL) equally across neurological diseases. This study examines the association between anxiety or depression and HRQoL in select neurological disorders., Methods: HRQoL was measured using the Short Form Health Survey (SF-12) in neurological patients: epilepsy (n = 279), migraine (n = 268), multiple sclerosis (MS) (n = 222), stroke (n = 204), and Parkinson's disease (PD) (n = 224). Depression and anxiety symptoms were assessed using the Patient Health Questionnaire (PHQ-9) and Hospital Anxiety and Depression Scale (HADS-A), respectively. Multiple linear regression was used to evaluate variables associated with the SF-12 mental health component (MCS) and physical health component scores (PCS). Pratt index was used to estimate the relative importance of anxiety and depression on HRQoL., Results: Anxiety and depression had the largest contribution to PCS in stroke and to MCS in epilepsy. Overall, anxiety and depression had a larger contribution to MCS as compared to PCS, except in stroke patients. Different patterns were seen across neurological diseases, with mental health variables strongly affecting MCS in all conditions, with also a sizable contribution to PCS in migraine, MS, and stroke., Conclusions: Anxiety and depression have varying impacts on HRQoL across neurological diseases. It is important for clinicians to be aware of how these patterns differ in each condition., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Health-related quality of life (HRQOL) assessment is an important health outcome for measuring the efficacy of treatments and interventions for coronary artery disease (CAD). HRQOL is known to improve over the first year after interventions for CAD, but there is limited knowledge of the changes in HRQOL beyond 1 year. We investigated heterogeneity in long-term trajectories of HRQOL in patients with CAD., Methods and Results: Data were obtained from 6226 patients identified from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease with at least 1-vessel CAD who underwent their first catheterization between 2006 and 2009. HRQOL was assessed using the Seattle Angina Questionnaire, a 19-item disease-specific measure of HRQOL for patients with CAD. Group-based trajectory analysis was used to identify various subgroups of Seattle Angina Questionnaire trajectories over time while adjusting for missing data through a longitudinal multiple imputation model. Multinomial logistic regression was used to identify the predictors of differences among the identified subgroups. Our analysis revealed significant improvements in HRQOL across all the 5 domains of Seattle Angina Questionnaire overtime for the whole data. Multitrajectory analyses revealed 4 HRQOL trajectory subgroups including high (25.1%), largely increased (32.3%), largely decreased (25.0%), and low (17.6%) trajectories. Age, sex, body mass index, diabetes mellitus, previous history of myocardial infarction, smoking, depression, anxiety, type of treatment received, and perceived social support were significant predictors of differences among these trajectory subgroups., Conclusions: This study highlights variations in longitudinal trajectories of HRQOL in patients with CAD. Despite overall improvements in HRQOL, about a quarter of our cohort experienced a significant decline in their HRQOL over the 5-year period. Understanding these HRQOL trajectories may help personalize prognostic information, identify patients and HRQOL domains on which clinical interventions are most beneficial, and support treatment decisions for patients with CAD., (© 2018 American Heart Association, Inc.)

Objective: Diagnosis of epilepsy is known to impact health-related quality of life (HRQOL) of children with new-onset epilepsy and can also influence their conceptualization and valuation of HRQOL construct, also known as response shift. This study investigates the presence of response shift in a cohort of children with new-onset epilepsy., Methods: Data are from the HEalth-Related QUality of Life in children with Epilepsy Study, a prospective cohort study of 373 children with new-onset epilepsy. Hypotheses about the presence of reconceptualization, reprioritization, and recalibration response shift were tested in the Quality of Life in Childhood Epilepsy (QOLCE-55) Questionnaire, a parent-reported, disease-specific HRQOL measure, using Oort's structural equation model between baseline and 1-year follow-up. Model fit was assessed using log-likelihood ratio test, root mean square error of approximation, and comparative fit index., Results: Small positive uniform recalibration response shift effects were observed on physical, emotional, and social functioning domains of the QOLCE-55, but negligibly small negative nonuniform recalibration response shift effect was observed on social functioning domain. There was no significant change in overall QOLCE-55 scores over time after adjusting for response shift effects., Significance: Parents of children with new-onset epilepsy are likely to positively recalibrate (upward bias) their assessments of their children's HRQOL over a 1-year period after diagnosis. This study highlights the potential benefits of response shift as a desired consequence in parents' perception of changes in HRQOL of children with new-onset epilepsy., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The diagnosis of epilepsy in children is known to impact the trajectory of their health-related quality of life (HRQOL) over time. However, there is limited knowledge about variations in longitudinal trajectories across multiple domains of HRQOL. This study aims to characterize the heterogeneity in HRQOL trajectories across multiple HRQOL domains and to evaluate predictors of differences among the identified trajectory groups in children with new-onset epilepsy. Data were obtained from the Health Related Quality of Life in Children with Epilepsy Study (HERQULES), a prospective multi-center study of 373 children newly diagnosed with new-onset epilepsy who were followed up over 2years. Child HRQOL and family factors were reported by parents, and clinical characteristics were reported by neurologists. Group-based multi-trajectory modeling was adopted to characterize longitudinal trajectories of HRQOL as measured by the individual domains of cognitive, emotional, physical, and social functioning in the 55-item Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55). Multinomial logistic regression was used to assess potential factors that explain differences among the identified latent trajectory groups. Three distinct HRQOL trajectory subgroups were identified in children with new-onset epilepsy based on HRQOL scores: "High" (44.7%), "Intermediate" (37.0%), and "Low" (18.3%). While most trajectory groups exhibited increasing scores over time on physical and social domains, both flat and declining trajectories were noted on emotional and cognitive domains. Less severe epilepsy, an absence of cognitive and behavioral problems, lower parental depression scores, better family functioning, and fewer family demands were associated with a "Higher" or "Intermediate" HRQOL trajectory. The course of HRQOL over time in children with new-onset epilepsy appears to follow one of three different trajectories. Addressing the clinical and psychosocial determinants identified for each pattern can help clinicians provide more targeted care to these children and their families., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Comparisons of population health status using self-report measures such as the SF-36 rest on the assumption that the measured items have a common interpretation across sub-groups. However, self-report measures may be sensitive to differential item functioning (DIF), which occurs when sub-groups with the same underlying health status have a different probability of item response. This study tested for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scales in population-based data using latent variable mixture models (LVMMs)., Methods: Data were from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective national cohort study. LVMMs were applied to the ten PF and five MH SF-36 items. A standard two-parameter graded response model with one latent class was compared to multi-class LVMMs. Multivariable logistic regression models with pseudo-class random draws characterized the latent classes on demographic and health variables., Results: The CaMos cohort consisted of 9423 respondents. A three-class LVMM fit the PF sub-scale, with class proportions of 0.59, 0.24, and 0.17. For the MH sub-scale, a two-class model fit the data, with class proportions of 0.69 and 0.31. For PF items, the probabilities of reporting greater limitations were consistently higher in classes 2 and 3 than class 1. For MH items, respondents in class 2 reported more health problems than in class 1. Differences in item thresholds and factor loadings between one-class and multi-class models were observed for both sub-scales. Demographic and health variables were associated with class membership., Conclusions: This study revealed DIF in population-based SF-36 data; the results suggest that PF and MH sub-scale scores may not be comparable across sub-groups defined by demographic and health status variables, although effects were frequently small to moderate in size. Evaluation of DIF should be a routine step when analysing population-based self-report data to ensure valid comparisons amongst sub-groups.

Purpose: Response shift (RS) may mask true change in health-related quality of life in longitudinal studies. People with chronic conditions may experience RS as they adapt to their disease, but it is unknown whether fluctuations in disease activity will influence the presence of RS. The study purpose was to test for RS in individuals with inflammatory bowel disease (IBD), a condition characterized by periods of symptom flares and remission., Methods: Data were from the Manitoba IBD Cohort Study (N = 388). Multi-group confirmatory factor analysis (MG-CFA) and a RS detection method based on structural equation modeling were used to test for reconceptualization, reprioritization, and recalibration RS in participants with consistent active, consistent inactive, and inconsistent disease activity over a 6-month period on the SF-36., Results: The MG-CFA revealed that a weak invariance model with equal factor loadings across groups was the best fit to the baseline SF-36 data. Reconceptualization, uniform recalibration, and non-uniform recalibration RS was detected in the consistent active group, but effect sizes were small. For the consistent inactive group, recalibration RS was observed and effect sizes were small to moderate. For the inconsistent disease activity group, small-to-moderate recalibration RS effects were observed. There was no evidence of reprioritization., Conclusions: Individuals with a chronic disease may exhibit RS even if they are not actively experiencing symptoms on a consistent basis. Heterogeneity in the type and magnitude of RS effects may be observed in chronic disease patients who experience changes in disease symptoms.

Purpose: Missing data due to attrition or item non-response can result in biased estimates and loss of power in longitudinal quality-of-life (QOL) research. The impact of missing data on response shift (RS) detection is relatively unknown. This overview article synthesizes the findings of three methods tested in this special section regarding the impact of missing data patterns on RS detection in incomplete longitudinal data., Methods: The RS detection methods investigated include: (1) Relative importance analysis to detect reprioritization RS in stroke caregivers; (2) Oort's structural equation modeling (SEM) to detect recalibration, reprioritization, and reconceptualization RS in cancer patients; and (3) Rasch-based item-response theory-based (IRT) models as compared to SEM models to detect recalibration and reprioritization RS in hospitalized chronic disease patients. Each method dealt with missing data differently, either with imputation (1), attrition-based multi-group analysis (2), or probabilistic analysis that is robust to missingness due to the specific objectivity property (3)., Results: Relative importance analyses were sensitive to the type and amount of missing data and imputation method, with multiple imputation showing the largest RS effects. The attrition-based multi-group SEM revealed differential effects of both the changes in health-related QOL and the occurrence of response shift by attrition stratum, and enabled a more complete interpretation of findings. The IRT RS algorithm found evidence of small recalibration and reprioritization effects in General Health, whereas SEM mostly evidenced small recalibration effects. These differences may be due to differences between the two methods in handling of missing data., Conclusions: Missing data imputation techniques result in different conclusions about the presence of reprioritization RS using the relative importance method, while the attrition-based SEM approach highlighted different recalibration and reprioritization RS effects by attrition group. The IRT analyses detected more recalibration and reprioritization RS effects than SEM, presumably due to IRT's robustness to missing data. Future research should apply simulation techniques in order to make conclusive statements about the impacts of missing data according to the type and amount of RS.

Purpose: Response shift (RS) is an important phenomenon that influences the assessment of longitudinal changes in health-related quality of life (HRQOL) studies. Given that RS effects are often small, missing data due to attrition or item non-response can contribute to failure to detect RS effects. Since missing data are often encountered in longitudinal HRQOL data, effective strategies to deal with missing data are important to consider. This study aims to compare different imputation methods on the detection of reprioritization RS in the HRQOL of caregivers of stroke survivors., Methods: Data were from a Canadian multi-center longitudinal study of caregivers of stroke survivors over a one-year period. The Stroke Impact Scale physical function score at baseline, with a cutoff of 75, was used to measure patient stroke severity for the reprioritization RS analysis. Mean imputation, likelihood-based expectation-maximization imputation, and multiple imputation methods were compared in test procedures based on changes in relative importance weights to detect RS in SF-36 domains over a 6-month period. Monte Carlo simulation methods were used to compare the statistical powers of relative importance test procedures for detecting RS in incomplete longitudinal data under different missing data mechanisms and imputation methods., Results: Of the 409 caregivers, 15.9 and 31.3 % of them had missing data at baseline and 6 months, respectively. There were no statistically significant changes in relative importance weights on any of the domains when complete-case analysis was adopted. But statistical significant changes were detected on physical functioning and/or vitality domains when mean imputation or EM imputation was adopted. There were also statistically significant changes in relative importance weights for physical functioning, mental health, and vitality domains when multiple imputation method was adopted. Our simulations revealed that relative importance test procedures were least powerful under complete-case analysis method and most powerful when a mean imputation or multiple imputation method was adopted for missing data, regardless of the missing data mechanism and proportion of missing data., Conclusions: Test procedures based on relative importance measures are sensitive to the type and amount of missing data and imputation method. Relative importance test procedures based on mean imputation and multiple imputation are recommended for detecting RS in incomplete data.

Objectives: Health-related quality of life (HRQOL) is an important indicator of well-being in patients with epilepsy. When assessing changes in HRQOL over time, some of the changes observed may be due to the patients' change in the internal standards, value, or meaning that they attribute to the HRQOL domain being measured, rather than actual change, a phenomenon known as response shift. Response shifts are increasingly recognized as an important explanation for the seemingly paradoxical or counterintuitive HRQOL results often observed in chronic conditions. We investigated the presence of changing values (reprioritization response shift) in data from a surgical randomized controlled trial of patients with temporal lobe epilepsy (TLE)., Methods: Eighty patients with TLE, who were randomized to surgical and medical treatment, provided data on the epilepsy-specific 31-item Quality of life in Epilepsy (QOLIE-31) questionnaire at baseline and 1 year after randomization. Reprioritization response shift among the seven QOLIE-31 domains was assessed using changes in the relative importance weights derived from logistic regression and discriminant analysis., Results: The relative importance analysis showed a statistically significant increase over time in the importance of social function, but a significant decrease in the relative importance of seizure worry. There were no significant changes in the relative importance of the remaining five domains in distinguishing between surgical and medical group over time., Significance: Patients that receive surgical treatment are more likely to experience a decrease in the valuation of seizure worry and an increase in the value of social function as compared to patients who are medically treated. Changes in expectation about seizure freedom and social function may confound the assessment of longitudinal change on these outcomes, and highlight the importance of assessing response shift and the limitations of assessing HRQOL at a single point in time or without a control group. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here., (Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.)

Objective: We examine improvement and worsening in quality of life (QOL) in terms of proportions achieving minimum clinically important change (MCID), and factors related to MCID, in patients with temporal lobe epilepsy randomized to medical or surgical treatment., Methods: Eighty patients with temporal lobe epilepsy randomized to surgical (n(1) = 40) or medical (n(2) = 40) therapy were followed for 12 months, reporting QOL at baseline, and at 6 and 12 months. Previously established thresholds for MCID across various general and epilepsy-specific QOL instruments were used to determine meaningful improvement (positive MCID) or worsening (negative MCID). Generalized linear mixed-effects models were used to compare MCID in both groups., Results: At 6 months, 56.0% of patients in the surgical group achieved positive MCID on the Quality of Life in Epilepsy (QOLIE)-89, as compared to 11.0% of those in the medical group (p < 0.001). On the QOLIE-31, 62.0% of the surgical group and 17.0% of the medical group achieved positive MCID (p < 0.001). Substantially more medically treated patients exhibited clinically significant worsening in QOL, as compared with those surgically treated. The respective medical versus surgical proportions with worsening were 36.67% versus 13.8% in QOLIE31, 20% versus 15% in Health Utility Index-III (HUI-III), and 30% versus 19% in Short Form-36 (SF-36) Mental Composite Score (MCS). The number of patients who need to undergo surgery for one additional person to have a meaningful improvement in the QOLIE-31 is two (number needed to treat = 2). The results also favored surgery using the generic HUI-III instrument, but not with the mental of physical function subscales of the SF-36., Significance: Significantly more patients in the surgical group achieved meaningful improvement in epilepsy-specific measures of QOL at 6 and 12 months compared to those in the medical group. Substantially more patients in the medical therapy group exhibited clinically significant worsening in their QOL assessed with epilepsy-specific and generic instruments. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here., (Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.)

Background: When patients experience a substantial health state change, they may undergo changes in the underlying meaning of their self-report to standardized outcome measures. These response shifts can reflect changes in the patient's internal standards, values or conceptualization of quality of life. We investigated the presence of changing values (reprioritization response shift) in a longitudinal cohort of spine surgery patients., Methods: Spinal decompression surgery patients (mean age 52 years; 39 % female, 36 % working) provided visual analogue scale (VAS) back and leg pain items, the Short-Form-36 (SF-36v1), and the Oswestry Disability Index (ODI) data pre- and post-surgery (n pre = 169; n 6weeks = 102; n 3months = 106; n 6months = 68). Improved and No-Effect patient groups were compared using the VAS minimally important difference (±15 points) as a cutoff. Reprioritization response shift detection was based on change in the relative importance of the SF-36 domains for group discrimination pre- and post-surgery., Results: The Improved group evidenced significant post-surgery differences from the No-Effect group on bodily pain, general health, physical functioning, social functioning, vitality, and the ODI. The relative importance analysis showed a differential effect with bodily pain (p < 0.01) and physical functioning (p < 0.05) becoming more important, and role physical (p < 0.01) becoming less important post-surgery in distinguishing the Improved group as compared to the No-Effect group. The Improved patients also evidenced stronger associations between bodily pain and physical functioning, vitality and general health (p < 0.05). The No-Effect group evidenced increased inter-correlations of bodily pain with social functioning, mental health, and general health (p < 0.05)., Conclusions: Patients who report clinically significant change in leg and back pain post-surgery using VAS pain scores are also distinguished by increased importance of bodily pain and physical functioning, and decreased importance of role physical. Bodily pain is primarily reflective of physical item response post-surgery among Improved patients, but reflects physical, social, and emotional item response among No-Effect patients. These changes in values may reflect a "moving goal post" in outcome assessment that complicates the interpretation of mean differences over time on standard spine outcome measures.

Purpose: Response shift (RS), a change in the meaning of an individual's self-evaluation of a target construct, such as health-related quality of life (HRQOL), can affect the interpretation of change in measures of the construct collected over time. This study proposes new statistical methods to test for reprioritization RS, in which the relative importance of HRQOL domains changes over time., Methods: The methods use descriptive discriminant analysis or logistic regression models and bootstrap inference to test for change in relative importance weights (method 1) or ranks (method 2) for discriminating between patient groups at two occasions. The methods are demonstrated using data from the Manitoba Inflammatory Bowel Disease (IBD) Cohort Study (n = 388). Reprioritization of domains from the IBD Questionnaire (IBDQ) and SF-36 was investigated for groups with active and inactive disease symptoms., Results: The IBDQ bowel symptoms and SF-36 bodily pain domains had the highest ranks for group discrimination. Using Method 1, there was evidence of reprioritization RS in the IBDQ social functioning domain and the SF-36 bodily pain and social functioning domains. Method 2 did not detect change for any of the domains., Conclusions: Compared to IBD patients without active disease symptoms, those with active symptoms were likely to change the meaning of their self-evaluations of pain and social interactions. Further research is needed to compare these new RS detection methods under a variety of data analytic conditions before recommendations about the optimal method can be made.

Purpose: In health-related quality of life (HRQOL) studies, data are often collected on multiple domains for two or more groups of study participants. Quantitative measures of relative importance, which are used to rank order the domains based on their ability to discriminate between groups, are an alternative to multiple tests of significance on the group differences. This study describes relative importance measures based on logistic regression (LR) and multivariate analysis of variance (MANOVA) models., Methods: Relative importance measures are illustrated using data from the Manitoba Inflammatory Bowel Disease (IBD) Cohort Study. Study participants with self-reported active (n = 244) and inactive (n = 105) disease were compared on 12 HRQOL domains from the Inflammatory Bowel Disease Questionnaire (IBDQ) and Medical Outcomes Study 36-item Short-Form (SF-36) Questionnaire., Results: All but two relative importance measures ranked the IBDQ bowel symptoms and emotional health domains as most important., Conclusions: MANOVA-based importance measures are recommended for multivariate normal data and when group covariances are equal, while LR measures are recommended for non-normal data and when the correlations among the domains are small. Relative importance measures can be used in exploratory studies to identify a small set of domains for further research.

Objective: The relationship between antiseizure medications (ASMs), which improve health outcomes by controlling seizures, and health‐related quality of life (HRQOL) is poorly understood and may involve intermediate variables. We evaluated the potential mediators of the association between ASMs and HRQOL. Methods: Data are from an outpatient registry of adult patients with epilepsy seen at the Foothills Medical Center, Calgary, Alberta, Canada. Quality of life was measured using the 10‐item Quality of Life in Epilepsy, and depression was measured using the Neurological Disorders Depression Inventory for Epilepsy. Propensity score matching was used to adjust for covariate imbalance between patients who received a single ASM (monotherapy) and those who received two or more ASMs (polytherapy) due to confounding. Mediation analysis was used to estimate the mediating effects of depression and ASM side effects on the association between patients' ASM polytherapy and HRQOL. Results: Of 778 patients included in this analysis, 274 (35.2%) were on two or more ASMs. Patient‐reported depression and ASM side effects jointly mediated the association between ASMs and HRQOL; these mediators accounted for 42% of the total average effect of ASM polytherapy (β = −13.6, 95% confidence interval = −18.2 to −8.6) on HRQOL. Significance: These findings highlight the importance of managing depression and ASM side effects for improving health outcomes of patients requiring treatment with ASMs. Intervention programs aimed at improving HRQOL of patients with epilepsy need to target these potential mediators. [ABSTRACT FROM AUTHOR]

Objective: To apply unsupervised machine learning to patient‐reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics. Methods: Consecutive outpatients seen at the Calgary Comprehensive Epilepsy Program outpatient clinics with complete patient‐reported outcome measures on quality of life, health state valuation, depression, and epilepsy severity and disability were studied. Data were acquired at each patient's first clinic visit. We used k‐means++ to segregate the population into three unique clusters. We then used multinomial regression to determine factors that were statistically associated with patient assignment to each cluster. Results: We identified 462 consecutive patients with complete patient‐reported outcome measure (PROM) data. Post hoc analysis of each cluster revealed one reporting elevated measures of psychosocial health on all five PROMs ("high psychosocial health" cluster), one with intermediate measures ("intermediate" cluster), and one with poor overall measures of psychosocial health ("poor psychosocial health" cluster). Failing to achieve at least 1 year of seizure freedom (relative risk [RR] = 4.34, 95% confidence interval [CI] = 2.13‐9.09) predicted placement in the "intermediate" cluster relative to the "high" cluster. In addition, failing to achieve seizure freedom, social determinants of health, including the need for partially or completely subsidized income support (RR = 6.10, 95% CI = 2.79‐13.31, P <.001) and inability to drive (RR = 4.03, 95% CI = 1.6‐10.00, P =.003), and a history of a psychiatric disorder (RR = 3.16, 95% CI = 1.46‐6.85, P =.003) were associated with the "poor" cluster relative to the "high" cluster. Significance: Seizure‐related factors appear to drive placement in the "intermediate" cluster, with social determinants driving placement in the "poor" cluster, suggesting a threshold effect. Precision intervention based on cluster assignment, with an initial emphasis on improving social support and careful titration of medications for those reporting the worst psychosocial health, could help optimize health for patients with epilepsy. [ABSTRACT FROM AUTHOR]

Background: Comparisons of population health status using self-report measures such as the SF-36 rest on the assumption that the measured items have a common interpretation across sub-groups. However, self-report measures may be sensitive to differential item functioning (DIF), which occurs when sub-groups with the same underlying health status have a different probability of item response. This study tested for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scales in population-based data using latent variable mixture models (LVMMs).Methods: Data were from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective national cohort study. LVMMs were applied to the ten PF and five MH SF-36 items. A standard two-parameter graded response model with one latent class was compared to multi-class LVMMs. Multivariable logistic regression models with pseudo-class random draws characterized the latent classes on demographic and health variables.Results: The CaMos cohort consisted of 9423 respondents. A three-class LVMM fit the PF sub-scale, with class proportions of 0.59, 0.24, and 0.17. For the MH sub-scale, a two-class model fit the data, with class proportions of 0.69 and 0.31. For PF items, the probabilities of reporting greater limitations were consistently higher in classes 2 and 3 than class 1. For MH items, respondents in class 2 reported more health problems than in class 1. Differences in item thresholds and factor loadings between one-class and multi-class models were observed for both sub-scales. Demographic and health variables were associated with class membership.Conclusions: This study revealed DIF in population-based SF-36 data; the results suggest that PF and MH sub-scale scores may not be comparable across sub-groups defined by demographic and health status variables, although effects were frequently small to moderate in size. Evaluation of DIF should be a routine step when analysing population-based self-report data to ensure valid comparisons amongst sub-groups. [ABSTRACT FROM AUTHOR]

Objective Seizure-related disability is an important contributor to health-related quality of life in persons with epilepsy. Yet, there is little information on patient-centered reports of seizure-related disability, as most studies focus on specific constructs of health-related disability, rather than epilepsy. We investigated how patients rate their own disability and how these ratings correlate with various clinical and sociodemographic characteristics. Methods In a prospective cohort of 250 adults with epilepsy consecutively enrolled in the Neurological Disease and Depression Study (NEEDs), we obtained a broad range of clinical and patient-reported measures, including patients' ratings of seizure-related disability and epilepsy severity using self-completed, single-item, 7-point response global assessment scales. Spearman's correlation, multiple linear regression, and mediation analyses were used to examine the association between seizure-related disability scores and clinical and demographic characteristics of persons with epilepsy. Results The mean age and duration of epilepsy was 39.8 and 16.7 years, respectively. About 29.5% of the patients reported their seizures as 'not at all disabling,' whereas 5.8% of the patients reported them as 'extremely disabling.' Age, seizure freedom at 1 year, anxiety, and epilepsy severity were identified as statistically significant predictors of disability scores. The indirect effects of age and seizure freedom, attributable to mediation through epilepsy severity, accounted for 25.0% and 30.3% of the total effects of these determinants on seizure-related disability, respectively. Significance Measuring seizure-related disability has heuristic value and it has important correlates and mediators that can be targeted for intervention in practice. Addressing modifiable factors associated with disability (e.g., seizure freedom and anxiety) could have a significant impact on decreasing the burden of disability in people with epilepsy. [ABSTRACT FROM AUTHOR]