Longitudinal and dynamic measurement invariance of the FACIT-Fatigue scale: an application of the measurement model of derivatives to ECOG-ACRIN study E2805.
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ABSTRACT: PURPOSE:While quality of life measures may be used to assess meaningful change and group differences, their scaling and validation often rely on a single occasion of measurement. Using the 13-item FACIT-Fatigue questionnaire at three timepoints, this study tests whether individual items change together in ways consistent with a general fatigue factor. METHODS:The measurement model of derivatives (MMOD) is a novel method for measurement evaluation that directly assesses whether a given factor structure accurately describes how individual test items change over time. MMOD transforms item-level longitudinal data into a set of orthogonal change scores, each one representing either a within-person longitudinal mean or a different type of longitudinal change. These change scores are then factor analyzed and tested for invariance. This approach is applied to the FACIT-Fatigue scale in a sample of patients with renal cell carcinoma treated on 'ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) study 2805. RESULTS:Analyses revealed strong evidence of unidimensionality, and apparent factorial invariance using traditional techniques. MMOD revealed a small but statistically significant difference in factor structure ([Formula: see text], [Formula: see text]), where factor loadings were weaker and more variable for measuring longitudinal change. CONCLUSIONS:The differences in factor structure were not large enough to substantially affect scale usage in this application, but they do reveal some variability across items in the FACIT-Fatigue in their ability to detect change. Future applications should consider differential sensitivity of individual items in multi-item scales, and perhaps even capitalize upon these differences by selecting items that are more sensitive to change.
SUBMITTER: Estabrook R
PROVIDER: S-EPMC6004788 | biostudies-literature | 2018 Jun
REPOSITORIES: biostudies-literature
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