Statistical Approaches for Modeling Radiologists' Interpretive Performance

Although much research has been conducted to understand the influence of interpretive volume on radiologists' performance of mammography interpretation, the published literature has been unable to achieve consensus on the volume standards required for optimal mammography accuracy. One potential contributing factor is that studies have used different statistical approaches to address the same underlying scientific question. Such studies have relied on multiple mammography interpretations from a sample of radiologists; thus, an important statistical issue is appropriately accounting for dependence, or correlation, among interpretations made by (or clustered within) the same radiologist. The aim of this review is to increase awareness about differences between statistical approaches used to analyze clustered data. Statistical frameworks commonly used to model binary measures of interpretive performance are reviewed, focusing on two broad classes of regression frameworks: marginal and conditional models. Although both frameworks account for dependence in clustered data, the interpretations of their parameters differ; hence, the choice of statistical framework may (implicitly) dictate the scientific question being addressed. Additional statistical issues that influence estimation and inference are also discussed, together with their potential impact on the scientific interpretation of the analysis. This work was motivated by ongoing research being conducted by the National Cancer Institute's Breast Cancer Surveillance Consortium; however, the ideas are relevant to a broad range of settings in which researchers seek to identify and understand sources of variability in clustered binary outcomes.