Defense Date

1992

Document Type

Thesis

Degree Name

Master of Science

Department

Biostatistics

First Advisor

Pippa M. Simpson

Abstract

Repeated measurement data or longitudinal data occur often in statistical applications. For example, in a clinical trial comparing the efficacy of a new treatment with that of a standard treatment, rather than measuring the main response variable only once on each patient, or subject, we can take several measurements over time on each subject.

A Repeated measurement study differs from a longitudinal study. The latter generally refers to any study in which one or more response variables are repeatedly measured over time. The former usually imposes some restrictions on the data. One common restriction is that each response variable must be measured at the same time points.

In this thesis, the discussion will be restricted to a repeated measurement study, which is defined as follows: a repeated measurement study is a study in which a univariate response variable is repeatedly measured at the same time points on each subject. It should be pointed out, however, that many of the methods discussed here can also be applied to more general longitudinal studies.

The analysis of repeated measurement data involves two major difficulties. The first problem is the dependence among successive observations made on the same subject. Multivariate methods modeling the joint distribution of the repeated measures over time have been developed to solve this difficulty. The other, probably the more severe problem is missing data. In repeated measurement studies, the data are collected over a period of time, which in some studies could be many years. Therefore, complete control over the circumstances under which measurements are obtained is not possible. The occurrence of missing data is more likely in repeated than in non-repeated measure studies, and is sometimes unavoidable.

In recent years, many methods for coping with the missing data problem in repeated measurement studies have emerged from various applications. The purpose of this thesis is to review and summarize these methods, apply some of them to a practical problem, and identify the needs of further research.

Comments

Scanned, with permission from the author, from the original print version, which resides in University Archives.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

3-9-2018

Included in

Biostatistics Commons

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