MULTIPLE IMPUTATION TO CORRECT FOR MEASUREMENT ERROR: Application to Chronic Disease Case Ascertainment in Administrative Health Databases

作者: Xue Yao

DOI:

关键词: StatisticsData miningObservational errorCase ascertainmentComputer scienceChronic disease

摘要:

参考文章(77)
Katherine M. Newton, Edward H. Wagner, Scott D. Ramsey, David McCulloch, Rhian Evans, Nirmala Sandhu, Connie Davis, The use of automated data to identify complications and comorbidities of diabetes: a validation study. Journal of Clinical Epidemiology. ,vol. 52, pp. 199- 207 ,(1999) , 10.1016/S0895-4356(98)00161-9
Raymond J Carroll, David Ruppert, Leonard A Stefanski, Ciprian M Crainiceanu, Measurement error in nonlinear models: a modern perspective Chapman & Hall/CRC. ,(2006)
Robert H. Friis, Thomas A. Sellers, Epidemiology for Public Health Practice ,(1996)
Karen Tu, Zhong-Liang Chen, Norman Rc Campbell, Finlay A McAlister, Karen J Cauch-Dudek, Accuracy of administrative databases in identifying patients with hypertension. Open Medicine. ,vol. 1, pp. 18- 26 ,(2007)
Steven C Bagley, Halbert White, Beatrice A Golomb, Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. Journal of Clinical Epidemiology. ,vol. 54, pp. 979- 985 ,(2001) , 10.1016/S0895-4356(01)00372-9
Juned Siddique, Ofer Harel, MIDAS: A SAS macro for multiple imputation using distance-aided selection of donors Journal of Statistical Software. ,vol. 29, pp. 1- 18 ,(2009) , 10.18637/JSS.V029.I09
Natalie A. Molodecky, Robert P. Myers, Herman W. Barkema, Hude Quan, Gilaad G. Kaplan, Validity of administrative data for the diagnosis of primary sclerosing cholangitis: a population-based study. Liver International. ,vol. 31, pp. 712- 720 ,(2011) , 10.1111/J.1478-3231.2011.02484.X