Regression models for analyzing radiological visual grading studies - an empirical comparison
2015 (English)In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, no 49Article in journal (Refereed) Published
Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients. Methods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects. Results: In general, the goodness of fit (AIC and McFaddens Pseudo R-2) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R-2 was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models. Conclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately.
Place, publisher, year, edition, pages
BIOMED CENTRAL LTD , 2015. Vol. 15, no 49
Image quality; Visual grading; Ordinal data; Regression models; Fixed effects; Random effects
IdentifiersURN: urn:nbn:se:liu:diva-123069DOI: 10.1186/s12880-015-0083-yISI: 000363921400001PubMedID: 26515510OAI: oai:DiVA.org:liu-123069DiVA: diva2:876585