Indiana 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.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/12305 |
| Date | 05 December 2016 |
| Creators | Zhan, Jia |
| Contributors | Shen, Changyu, Li, Xiaochun, Li, Lingling, Xu, Huiping, Wessel, Jennifer |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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