Indiana University-Purdue University Indianapolis (IUPUI) / Cluster randomization is frequently used in clinical trials for convenience of inter
ventional implementation and for reducing the risk of contamination. The opera
tional convenience of cluster randomized trials, however, is gained at the expense
of reduced analytical power. Compared to individually randomized studies, cluster
randomized trials often have a much-reduced power. In this dissertation, I consider
ways of enhancing analytical power with historical trial data. Specifically, I introduce
a hierarchical Bayesian model that is designed to incorporate available information
from previous trials of the same or similar interventions. Operationally, the amount
of information gained from the previous trials is determined by a Kullback-Leibler
divergence measure that quantifies the similarity, or lack thereof, between the histor
ical and current trial data. More weight is given to the historical data if they more
closely resemble the current trial data. Along this line, I examine the Type I error
rates and analytical power associated with the proposed method, in comparison with
the existing methods without utilizing the ancillary historical information. Similarly,
to design a cluster randomized trial, one could estimate the power by simulating trial
data and comparing them with the historical data from the published studies. Data
analytical and power simulation methods are developed for more general situations
of cluster randomized trials, with multiple arms and multiple types of data following
the exponential family of distributions. An R package is developed for practical use
of the methods in data analysis and trial design.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/14611 |
| Date | 07 August 2017 |
| Creators | Xiao, Shan |
| Contributors | Tu, Wanzhu, Liu, Ziyue |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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