Date of Award

1-1-2014

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Epidemiology and Biostatistics

Program

Biostatistics

Content Description

1 online resource (xii, 89 pages) : illustrations (some color)

Dissertation/Thesis Chair

Recai M Yucel

Committee Members

Gregory A DiRienzo, Tao Lu

Keywords

Causal Inference, Generalized Estimating Equation, Longitudinal Analysis, Matching, Missing Data Analysis, Propensity Score Analysis, Outliers (Statistics), Statistical matching, Matching theory, Observation (Scientific method), Stochastic processes

Subject Categories

Biostatistics

Abstract

An observational study is an empirical investigation of treatment effect when randomized experimentation is not ethical or feasible (Rosenbaum 2009). Observational studies are common in real life due to the following reasons: a) randomization is not feasible due to the ethical or financial reason; b) data are collected from survey or other resources where the object and design of the study has not been determined (e.g. retrospective study using administrative records); c) little knowledge on the given region so that some preliminary studies of observational data are conducted to formulate hypotheses to be tested in subsequent experiments. When statistical analysis are done using observational studies, the following issues need to be considered: a) the lack of randomization may lead to a selection bias; b) representativeness of sampling with respect to the problem under consideration (e.g. study of factors influencing a rare disease using a nationally representative survey with respective to race, income, and gender but not with respect to the rare disease condition).We will use the following sample to illustrate the challenges of observational studies and possible mitigation measures.

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