Nonparametric verification bias corrected inference fo ROC analysis

 
   Lead Investigator:    Monica Chiogna
   Institution      :    University of Padova
   E-Mail           :    monica@stat.unipd.it
   Proposal ID      :    520



   Proposal Description:
   Some methods have been proposed to correct verification bias, but they require parametric models for the (conditional) probability of disease and/or the (conditional) probability of verification. A wrong specification of such parametric models can affect the behavior of the estimators, which can be      inconsistent. To avoid misspecification problems, in this project we study a fully nonparametric method for the estimation of the AUC of a continuous test under verification bias.  The method is based on nearest-neighbor imputation and adopts generic smooth regression models for both the probability that a subject is diseased and the probability that it is verified.  The new AUC estimator is consistent and asymptotically normal under the assumption that the true disease status, if missing, is missing at random (MAR).                        Specific objective: Comparison of our proposal with existing methods using these data. We want to investigate the 1-year progression from amnestic MCI to dementia, and find out how well the baseline MMSE score classifies the patients who progressed to dementia and those who do not in 1 year. So, our variable of interest is MMSE. The "disease" in this study is progression to dementia. If a patient made a visit about 1 year (within the 6???18 months window) after the first visit, his/her cognitive status is observed with D=1 indicating progression to dementia and 0 otherwise. The disease status is missing if the patient only made the baseline (first) visit, or the follow-up visits were all outside the 6???18 months window.