Defense Date
4-8-2016
Graduation Date
Spring 2016
Availability
Immediate Access
Submission Type
thesis
Degree Name
MS
Department
Computational Mathematics
School
McAnulty College and Graduate School of Liberal Arts
Committee Chair
Frank D'Amico
Committee Member
John Kern
Keywords
Longitudinal Data, Mixed Effects Models
Abstract
A common goal in medical research is to determine the effect that a treatment has on subjects over time. Unfortunately, the analysis of data from such clinical trials often omits several aspects of the study design, leading to incorrect or misleading conclusions. In this paper, a major objective is to show via case studies that randomized controlled trials with longitudinal designs must account for correlation and clustering among observations in order to make proper statistical inference. Further, the effects of outliers in a multi-center, randomized controlled trial with multiple layers of clustering are examined and strategies for detecting and dealing with outlying observations and clusters are discussed.
Format
Language
English
Recommended Citation
Baumgardner, A. (2016). Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/296