Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients

To illustrate how structural equation modeling (SEM) can be used for response shift detection with random measurement occasions and health state operationalized as fixed group membership (Study 1) or with fixed measurement occasions and health state operationalized as time-varying covariates (Study 2). In Study 1, we explored seven items of the Performance Scales measuring physical and mental aspects of perceived disability of 771 stable, 629 progressive, and 1,552 relapsing MS patients. Time lags between the three measurements varied and were accounted for by introducing time since diagnosis as an exogenous variable. In Study 2, we considered the SF-12 scales measuring physical and mental components of HRQoL of 1,767 patients. Health state was accounted for by exogenous variables relapse (yes/no) and symptoms (worse/same/better). In Study 1, progressive and relapsing patients reported greater disability than stable patients but little longitudinal change. Some response shift was found with stable and relapsing patients. In Study 2, relapse and symptoms were associated with HRQoL, but no change and only little response shift was found. While small response shifts were found, they had little impact on the evaluation of true change in performance and HRQo

Using classification and regression tree modelling to investigate response shift patterns in dentine hypersensitivity

BACKGROUND: Dentine hypersensitivity (DH) affects people's quality of life (QoL). However changes in the internal meaning of QoL, known as Response shift (RS) may undermine longitudinal assessment of QoL. This study aimed to describe patterns of RS in people with DH using Classification and Regression Trees (CRT) and to explore the convergent validity of CRT with the then-test and ideals approaches. METHODS: Data from an 8-week clinical trial of mouthwashes for dentine hypersensitivity (n = 75) using the Dentine Hypersensitivity Experience Questionnaire (DHEQ) as the outcome measure, were analysed. CRT was used to examine 8-week changes in DHEQ total score as a dependent variable with clinical status for DH and each DHEQ subscale score (restrictions, coping, social, emotional and identity) as independent variables. Recalibration was inferred when the clinical change was not consistent with the DHEQ change score using a minimally important difference for DHEQ of 22 points. Reprioritization was inferred by changes in the relative importance of each subscale to the model over time. RESULTS: Overall, 50.7% of participants experienced a clinical improvement in their DH after treatment and 22.7% experienced an important improvement in their quality of life. Thirty-six per cent shifted their internal standards downward and 14.7% upwards, suggesting recalibration. Reprioritization occurred over time among the social and emotional impacts of DH. CONCLUSIONS: CRT was a useful method to reveal both, the types and nature of RS in people with a mild health condition and demonstrated convergent validity with design based approaches to detect RS

Testing the measurement invariance of the EORTC QLQ-C30 across primary cancer sites using multi-group confirmatory factor analysis

Purpose: The EORTC Quality of Life Questionnaire is a widely used cancer-specific quality of life instrument comprising a core set of 30 items (QLQ-C30) supplemented by cancer site-specific modules. The purpose of this paper was to examine the extent to which the conventional multi-item domain structure of the QLQ-C30 holds across patients with seven different primary cancer sites. Methods: Multi-group confirmatory factor analysis was used to test whether a measurement model of the QLQ-C30 was invariant across cancer sites. Configural (same patterns of factor loadings), metric (equivalence of factor loadings) and scalar (equivalence of thresholds) invariance amongst the cancer site groups were assessed (N = 1,906) by comparing the fit of a model with these parameters freely estimated to a model where estimates were constrained to be equal for the corresponding items in each group. Results: All groups exhibited good model fit except for the prostate group, which was excluded. Only 1 of 576 parameters was found to differ between primary sites: specifically, the first threshold of Item 1 in the breast cancer group exhibited non-invariance. In a post hoc analysis, several instances of non-invariance by treatment status (baseline, on-treatment, off-treatment) were observed. Conclusions: Given only one instance of non-invariance between cancer sites, there is a reason to be confident in the validity of conclusions drawn when comparing QLQ-C30 domain scores between different sites and when interpreting the scores of heterogeneous samples, although future research should assess the potential impact of confounding variables such as treatment and gender