Defense Date

2012

Document Type

Thesis

Degree Name

Master of Science

Department

Biostatistics

First Advisor

Donna McClish

Abstract

Comparing samples from different populations can be biased by confounding. There are several statistical methods that can be used to control for confounding. These include; multiple linear regression, propensity score matching, propensity score/logit of propensity score as a single covariate in a linear regression model, stratified analysis using propensity score quintiles, weighted analysis using propensity scores or trimmed scores. The data were from two studies of a dietary intervention (FIBERR and RNP). The outcome variable was change from baseline to one month for eight outcome measures; fat, fiber, and fruits/ vegetables behavior, fat, fiber, and fruits/vegetables intentions, fat and fruits/vegetables self-efficacy. It was found that the propensity score matching and the quintiles analysis were the two best methods for analyzing this dataset. The weighted analyses were the worst of all the methods compared in analyzing this particular dataset.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

August 2012

Included in

Biostatistics Commons

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