Item response modelling for clinical and laboratory testing

BACKGROUND: Item response models using exponential modelling are more sensitive than classical linear methods for making predictions from psychological questionnaires. OBJECTIVE: To assess whether they can also be used for making predictions from quality of life questionnaires and clinical and laboratory diagnostic-tests. METHODS: Of 1000 anginal patients assessed for quality of life and 1350 patients assessed for peripheral vascular disease with diagnostic laboratory tests, items response modelling was applied using the Latent Trait Analysis program -2 of Uebersax. RESULTS: The 32 different response patterns obtained from test batteries of five items produced 32 different quality of life scores ranging from 3·4% to 74·5% and 32 different levels of peripheral vascular disease ranging from 9·9% to 83·5% with overall mean scores, by definition, of 50%, whereas the classical method of analysis produced the discrete scores of only 0-5. The item response models produced an adequate fit for the data as demonstrated by chi-square goodness of fit values/degrees of freedom of 0·86 and 0·64. CONCLUSIONS: 1 Quality of life assessments and diagnostic tests can be analysed through item response modelling, and provide more sensitivity than do classical linear models. 2 Item response modelling can change largely qualitative data into fairly accurate quantitative data, and can, even with limited sets of items, produce fairly accurate frequency distribution patterns of quality of life, severity of disease and other latent traits