Shen, ChangyuLi, XiaochunZhan, JiaLi, LinglingXu, HuipingWessel, Jennifer2017-04-212019-04-032016-12-05https://hdl.handle.net/1805/12305http://dx.doi.org/10.7912/C2/2793Indiana University-Purdue University Indianapolis (IUPUI)Observational studies offer unique advantages over randomized clinical trials (RCTs) in many situations where RCTs are not feasible or suffer from major limitations such as insufficient sample sizes and narrowly focused populations. Because observational data are relatively easy and inexpensive to access, and contain rich and comprehensive demographic and medical information on large and representative populations, they have played a major role in the assessment of the effectiveness and safety of medical interventions. However, observational data also have the challenges of higher rates of missing data and the confounding effect. My proposal is on the development of three statistical methods to address these challenges. The first method is on the refinement and extension of a multiply robust (MR) estimation procedure that simultaneously accounts for the confounding effect and missing covariate process, where we derived the asymptotic variance estimator and extended the method to the scenario where the missing covariate is continuous. The second method focuses on the improvement of estimation precision in an RCT by a historical control cohort. This was achieved through augmenting the conventional effect estimator with an extra mean zero (approximately) term correlated with the conventional effect estimator. In the third method, we calibrated the hidden database bias of an electronic medical records database and utilized an empirical Bayes method to improve the accuracy of the estimation of the risk of acute myocardial infarction associated with a drug by borrowing information from other drugs.en-USMissing dataObservational studyPropensity scoreThesis10.7912/C29308