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Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data. | LitMetric

Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data.

J Stat Comput Simul

Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, SUNY, One University Place, Rensselaer, NY, USA.

Published: January 2015

AI Article Synopsis

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4568435PMC
http://dx.doi.org/10.1080/00949655.2014.983111DOI Listing

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