title: Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data.
abstract: Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms.
authors.affiliation: Department of Biostatistics, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Turkey.
authors.forename: Jale
authors.initials: J
authors.lastname: Karakaya
authors.affiliation: Department of Biostatistics, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Turkey.
authors.forename: Erdem
authors.initials: E
authors.lastname: Karabulut
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, SUNY, One University Place, Rensselaer, NY, USA.
authors.forename: Recai M
authors.initials: RM
authors.lastname: Yucel
keywords: ROC; diagnostic test; missing data; multiple imputation; sensitivity