Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering

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.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2016

Author dc:creator

• Baumgardner, Adam

Contributors dc:contributor

• Frank D'Amico
• John Kern

• Longitudinal Data
• Mixed Effects Models

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/296

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1300