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Xiaoyan Lin, University of South Carolina
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| Title: Simultaneous Modeling of Propensity for Disease, Rater Bias and Rater Diagnostic Skill in Dichotomous Subjective Rating Experiments |
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Abstract: Many disease diagnoses involve subjective judgments. For example, through the inspection of a mammogram, MRI, radiograph, ultrasound image, etc., the clinician himself becomes part of the measuring instrument. Variability among raters examining the same item injects variability into the entire diagnostic process and thus adversely affect the utility of the diagnostic process itself. To reduce diagnostic errors and improve the quality of diagnosis, it is very important to quantify inter-rater variability, to investigate factors affecting the diagnostic accuracy, an to reduce the inter-rater variability over time. This paper focuses on a subjective binary decision process. A hierarchical model linking data on rater opinions with patient disease-development outcomes is proposed. The model allows for the quantification of patient-specific disease severity and rater-specific bias and diagnostic ability. The model can be used in an ongoing setting in a variety of ways, including calibration of rater opinions (estimation of the probability of disease development given opinions) and quantification of rater-specific sensitivities and specificities. Bayesian computational algorithm is developed. An extensive simulation study is conducted to evaluate the proposed method, and the proposed method is illustrated by a mammogram data set.
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