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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Clinical registries, which capture information about the health and healthcare use of patients with a health condition or treatment, often contain patient-reported outcomes (PROs) that provide insights about the patient's perspectives on their health. Missing data can affect the value of PRO data for healthcare decision-making. We compared the precision and bias of several missing data methods when estimating longitudinal change in PRO scores.Methods: This research conducted analyses of clinical registry data and simulated data. Registry data were from a population-based regional joint replacement registry for Manitoba, Canada; the study cohort consisted of 5631 patients having total knee arthroplasty between 2009 and 2015. PROs were measured using the 12-item Short Form Survey, version 2 (SF-12v2) at pre- and post-operative occasions. The simulation cohort was a subset of 3000 patients from the study cohort with complete PRO information at both pre- and post-operative occasions. Linear mixed-effects models based on complete case analysis (CCA), maximum likelihood (ML) and multiple imputation (MI) without and with an auxiliary variable (MI-Aux) were used to estimate longitudinal change in PRO scores. In the simulated data, bias, root mean squared error (RMSE), and 95% confidence interval (CI) coverage and width were estimated under varying amounts and types of missing data.Results: Three thousand two hundred thirty (57.4%) patients in the study cohort had complete data on the SF-12v2 at both occasions. In this cohort, mixed-effects models based on CCA resulted in substantially wider 95% CIs than models based on ML and MI methods. The latter two methods produced similar estimates and 95% CI widths. In the simulation cohort, when 50% of the data were missing, the MI-Aux method, in which a single hypothetical auxiliary variable was strongly correlated (i.e., 0.8) with the outcome, reduced the 95% CI width by up to 14% and bias and RMSE by up to 50 and 45%, respectively, when compared with the MI method.Conclusions: Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable(s) may be challenging. [ABSTRACT FROM AUTHOR]