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

Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression

ABSTRACT: BACKGROUND: Differential item functioning (DIF) methods can be used to determine whether different subgroups respond differently to particular items within a health-related quality of life (HRQoL) subscale, after allowing for overall subgroup differences in that scale. This article reviews issues that arise when testing for DIF in HRQoL instruments. We focus on logistic regression methods, which are often used because of their efficiency, simplicity and ease of application. METHODS: A review of logistic regression DIF analyses in HRQoL was undertaken. Methodological articles from other fields and using other DIF methods were also included if considered relevant. RESULTS: There are many competing approaches for the conduct of DIF analyses and many criteria for determining what constitutes significant DIF. DIF in short scales, as commonly found in HRQL instruments, may be more difficult to interpret. Qualitative methods may aid interpretation of such DIF analyses. CONCLUSIONS: A number of methodological choices must be made when applying logistic regression for DIF analyses, and many of these affect the results. We provide recommendations based on reviewing the current evidence. Although the focus is on logistic regression, many of our results should be applicable to DIF analyses in general. There is a need for more empirical and theoretical work in this are

Interpretation of differential item functioning analyses using external review

Differential item functioning (DIF) analyses are used to determine whether certain groups respond differently to a particular item of a test or questionnaire; however, these do not explain the reasons for observed response differences. Many studies have used external reviews of items, sometimes using blinded reviewers, to help interpret these results. The authors conducted a literature review of this topic to describe the current usage of external reviews alongside DIF analyses. It concentrated on studies of health-related quality of life instruments, but studies in other fields were also considered. Relatively few examples of blinded item reviews were identified, and these were mostly from educational studies. A case study using blinded bilingual reviewers alongside translation DIF analyses of a health-related quality of life instrument is described. Future researchers should consider conducting external item reviews alongside DIF analyse