46 research outputs found
Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30: a confirmatory versus exploratory approach
Background: Multi attribute utility instruments (MAUIs) are preference-based measures that
comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility
value to each health state in the HSCS. When developing a MAUI from a health-related quality
of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting
a subset of domains and items because HRQOL questionnaires typically have too many items
to be amendable to the valuation task required to develop the scoring algorithm for a MAUI.
Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for
deriving a MAUI from a HRQOL measure.
Aim: To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient
than EFA to derive a HSCS from the European Organisation for the Research and Treatment
of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its
well-established domain structure.
Methods: QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative
radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure
of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQC30
structure and views of both patients and clinicians on which are the most relevant items.
Dimensions determined by EFA or CFA were then subjected to Rasch analysis.
Results: CFA results generally supported the proposed QLQ-C30 structure (comparative fit
index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA
revealed fewer factors and some items cross-loaded on multiple factors. Further assessment
of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with
those detected by CFA.
Conclusion: CFA was more appropriate and efficient than EFA in producing clinically interpretable
results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest
that CFA should be recommended generally when deriving a preference-based measure from a
HRQOL measure that has an established domain structure
Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression
Erratum to: Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format.
© 2017, Springer International Publishing Switzerland. In this article by R. Norman et al., the article by M. T. King et al. is cited as Reference 10, as ‘Submitted’ and ‘Under Review’. However, the Reference 10 should appear with year, volume and page numbers as: King et al., Quality of Life Research (2016); 25(3):625-636. Also an error was found in Table 1 in the reported wording of the Physical Functioning item. The error and correction are described below. The error was limited to Table 1. The survey described in the paper used the correct labelling, and the validity of the analysis is therefore unaffected by the error
"It ain’t over till the fat lady sings": a response to Cameron N. McIntosh, improving the evaluation of model fit in confirmatory factor analysis
Psykisk velbefindende og træthed prædikterede risiko for recidiv og død efter brystkræft - sekundærpublikation
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
Are importance–satisfaction discrepancies with regard to ratings of specific health-related quality-of-life aspects valid indicators of disease- and treatment-related distress among patients with endocrine gastrointestinal tumours?
Comparing higher order models for the EORTC QLQ-C30
Purpose To investigate the statistical fit of alternative higher order models for summarizing the health-related quality of life profile generated by the EORTC QLQ-C30 questionnaire. Methods A 50% random sample was drawn from a dataset of more than 9,000 pre-treatment QLQ-C30 v 3.0 questionnaires completed by cancer patients from 48 countries, differing in primary tumor site and disease stage. Building on a "standard" 14-dimensional QLQ-C30 model, confirmatory factor analysis was used to compare 6 higher order models, including a 1-dimensional (1D) model, a 2D "symptom burden and function" model, two 2D "mental/physical" models, and two models with a "formative" (or "causal") formulation of "symptom burden," and "function." Results All of the models considered had at least an "adequate" fit to the data: the less restricted the model, the better the fit. The RMSEA fit indices for the various models ranged from 0.042 to 0.061, CFI’s 0.90-0.96, and TLI’s from 0.96 to 0.98. All chi-square tests were significant. One of the Physical/Mental models had fit indices superior to the other models considered. Conclusions The Physical/Mental health model had the best fit of the higher order models considered, and enjoys empirical and theoretical support in comparable instruments and applications