A MULTIPLE IMPUTATION APPROACH TO EVALUATE THE ACCURACY OF DIAGNOSTIC TESTS IN PRESENCE OF MISSING VALUES

Diagnostic tests are used to determine the presence or absence of a disease. Diagnostic accuracy is the main tool to evaluate a test. Four accuracy measures are used to evaluate how well the results of the test under evaluation (index test) agree with the outcome of the reference test (gold standard) These measures are sensitivity, specificity, positive predictive value and negative predictive value. Some subjects are only measured by a subset of tests which result in missing values. This leads to biased results. The mechanism of missing data could be missing completely at random (MCA), missing at random (MAR), or missing not at random (MNAR). Various methods such as the complete-case analysis (CCA) and the maximum likelihood (ML) method are used to handle missing data. Also, imputation methods could be used. The article aims to use a multiple imputation approach to evaluate binary diagnostic tests with missing data under the MCAR mechanism The proposed approach is applied to a real data set. Also, a simulation study is conducted to evaluate the performance of the proposed approach.