Poisson regression models for interval censored count data.

In this dissertation, we develop Bayesian models for interval censored Poisson counts in the presence of zero inflation and missing data. As a motivating example, we consider data arising from a Human Immunodeficiency Virus (HIV) vaccine trial featuring imprecise counts, missing data, and an abundance of values which are either exactly observed to be zero or are left censored. We compare frequentist and Bayesian generalized linear mixed models of the lower limits of the intervals when the data contain no missing values. We then propose a likelihood which models the lower and upper limits of the observed intervals and accomodates zero inflation. Next, we present a simulation study comparing models of the intervals or lower limits to the precise count models. Finally, we apply the model of interval-censored Poisson counts to the HIV data and discuss the conclusions that are drawn from each analysis.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2011

Author dc:creator

• Watson, Sydeaka P.

Advisors dc:contributor.advisor

• Seaman, John Weldon, Jr., 1956-
• Stamey, James D.

• Poisson regression.
• Bayesian models.
• Interval censored data.
• Generalized linear mixed models.
• Human immunodeficiency virus (HIV) vaccine.

Statement dc:rights

• Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/8176

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/8176