الفهرس 
Title : A Multiple Imputation Approach to Evaluate Diagnostic Tests in Presence of Missing Values
AbstractOnly 14 pages are availabe for public view
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Abstract
Diagnostic tests are used to determine the presence or absence of a disease. Determining diagnostic accuracy is a main point in the evaluation of a test because the accurate diagnosis of adiseaseis often the first step toward its treatment and prevention.Some accuracy measures such as sensitivity, specificity, positive predictive value and negative predictive value explain how well the results of the test under evaluation (index test) agree with the outcome of the reference (gold standard) test. Missing data is a common issue in all types of medical research and unavoidable instudies designed to compare the accuracy of diagnostic testswhere some subjects are only measured by a subset of testswhich produces potential bias, even in well conducted studies.Determining the pattern and mechanism of missing data(MCAR, MAR, MNAR)is an important matter in the analysis of missing data.Various methods such as the complete-case analysis (CCA) method and the maximum likelihood(ML) method are used to handle missing data. Also,imputation methods could be used.The current studyaims to use a multiple imputation approach to evaluate binary diagnostic tests with missing data under the MCAR assumption.The proposed approach is applied on a real data set. Also, a simulation study is conducted to evaluate the performance of the proposed approach