JSM 2004 - Toronto

Abstract #300689

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Activity Number: 405
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #300689
Title: Stochastic Models for Compliance Analysis and Applications
Author(s): Junfeng Sun*+ and Haikady N. Nagaraja
Companies: Ohio State University and Ohio State University
Address: 650 Trumbull Ct., Columbus, OH, 43210,
Keywords: electronic event-monitoring ; compliance ; stochastic modeling ; Markov-dependent mixture models ; empirical Bayes
Abstract:

Compliance is the extent to which a patient follows the prescribed regimen. It is crucial to accurately measure compliance in both clinical trials and medical practice. It has received a lot of attention in the medical literature. However, little effort has been made to study the statistical properties of various compliance metrics. This study utilizes the information-rich electronic event monitoring (EEM) data and studies the statistical properties of several clinically meaningful compliance metrics. Markov-dependent mixture models are applied for the metrics based on continuous data. To pool the data from different patients, we use empirical Bayes approaches. As an illustration, we apply these metrics to an AIDS clinical trial. Practical applications of these compliance metrics will also be discussed.


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