Advisor

Lalonde, Trent L.

Committee Member

Schaffer, Jay Ryan, 1969-

Committee Member

Hutchinson, Susan R.

Committee Member

Mostowfi, Mehrgan

Department

Applied Statistics and Research Methods

Institution

University of Northern Colorado

Type of Resources

Text

Place of Publication

Greeley (Colo.)

Publisher

University of Northern Colorado

Date Created

8-2017

Genre

Thesis

Extent

166 pages

Digital Origin

Born digital

Abstract

Longitudinal data occur in different fields such as biomedical and health studies, education, engineering, and social studies. Planning advantageous research projects with both high power and minimum sample size is an important step in any study. The extensive use of longitudinal data in different fields and the importance of their power estimation, yet the limited resources about their respective power estimation tools, made it worthwhile to study their power estimation techniques. The presence of time-dependent covariates triggers the need to use more efficient models such as generalized method of moments than the existing models which are based on generalized estimating equations. Not taking into consideration the correlation among observations and the covariates that change over time while calculating power and minimum sample size will cause expensive research being conducted without using data that are capable of answering the research questions (Williams, 1995). Two different power estimation and minimum sample size calculation techniques for longitudinal data in the presence of time-dependent covariate using generalized method of moments approaches are constructed in this study and their performances are evaluated.

Notes

Recipient of Dean's Citation for Excellence

Degree type

PhD

Degree Name

Doctoral

Language

English

Local Identifiers

Ramezani_unco_01610D_10596

Rights Statement

Copyright is held by the author.

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