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Stochastic models for compliance ana...

Sun, Junfeng.

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Stochastic models for compliance analysis and applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic models for compliance analysis and applications./
作者:	Sun, Junfeng.
面頁冊數:	150 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2664.
Contained By:	Dissertation Abstracts International66-05B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3176410
ISBN:	9780542158773

Stochastic models for compliance analysis and applications.
Sun, Junfeng.

Stochastic models for compliance analysis and applications. - 150 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2664.

Thesis (Ph.D.)--The Ohio State University, 2005.

Compliance is the extent to which a patient follows the prescribed regimen. Good compliance is crucial in maintaining the drug concentration in the body, and is thus very important in both clinical trials and medical practice. Even though many different compliance indices have been proposed in the literature, few studies have been devoted to the study of the compliance process. There is no published systematic study of the statistical properties of these compliance indices. We utilize the information-rich electronic event monitoring (EEM) data, build realistic stochastic models to describe them, and study the statistical properties of several clinically meaningful compliance indices. For discrete compliance data, we use stationary Markov chains to model the dependence structure and empirical Bayes approach to account for the variation among patients. The indices based on discrete data are the percentage of compliant days and the percentage of doses taken. We also study several indices based on inter-dosing times: the therapeutic coverage, the delayed medication index, the premature medication index, the timing error, and the percentage of time in drug holidays . We apply Markov-dependent mixture models to describe the inter-dosing times. To construct a more biologically meaningful index of compliance, we combine the pharmacokinetic (PK) model of the drug with the inter-dosing times. We establish asymptotic normality of the various indices under the proposed models and construct hypothesis tests to compare the compliance levels of patients or different groups of patients. We illustrate our methodology through an analysis of a data set from an AIDS clinical trial.

ISBN: 9780542158773Subjects--Topical Terms:

517247
Statistics.

Stochastic models for compliance analysis and applications.
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520 $a Compliance is the extent to which a patient follows the prescribed regimen. Good compliance is crucial in maintaining the drug concentration in the body, and is thus very important in both clinical trials and medical practice. Even though many different compliance indices have been proposed in the literature, few studies have been devoted to the study of the compliance process. There is no published systematic study of the statistical properties of these compliance indices. We utilize the information-rich electronic event monitoring (EEM) data, build realistic stochastic models to describe them, and study the statistical properties of several clinically meaningful compliance indices. For discrete compliance data, we use stationary Markov chains to model the dependence structure and empirical Bayes approach to account for the variation among patients. The indices based on discrete data are the percentage of compliant days and the percentage of doses taken. We also study several indices based on inter-dosing times: the therapeutic coverage, the delayed medication index, the premature medication index, the timing error, and the percentage of time in drug holidays . We apply Markov-dependent mixture models to describe the inter-dosing times. To construct a more biologically meaningful index of compliance, we combine the pharmacokinetic (PK) model of the drug with the inter-dosing times. We establish asymptotic normality of the various indices under the proposed models and construct hypothesis tests to compare the compliance levels of patients or different groups of patients. We illustrate our methodology through an analysis of a data set from an AIDS clinical trial.
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