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Optimal designs for the detection of...

Kupchak, Petro Iwan.

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Optimal designs for the detection of drug interaction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimal designs for the detection of drug interaction./
Author:	Kupchak, Petro Iwan.
Description:	179 p.
Notes:	Source: Dissertation Abstracts International, Volume: 61-11, Section: B, page: 5948.
Contained By:	Dissertation Abstracts International61-11B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ53873
ISBN:	0612538737

Optimal designs for the detection of drug interaction.
Kupchak, Petro Iwan.

Optimal designs for the detection of drug interaction. - 179 p.

Source: Dissertation Abstracts International, Volume: 61-11, Section: B, page: 5948.

Thesis (Ph.D.)--University of Toronto (Canada), 2000.

Methods of statistical inference are developed which can be used to test the hypothesis of dose additivity at a specified dose combination, given that one has access to binary dose-response data collected from an experiment where two drugs are administered in varying doses to a group of subjects. The distributional properties of the estimate of the interaction parameter in a bivariate logistic dose-response model are also investigated, via simulation studies. Using the principles of optimal design theory for nonlinear dose-response models, design measures are constructed which minimize various criteria related to the precision with which one can quantify the degree of interaction between two drugs. Given that one is allowed to choose from dose combinations such that the associated probability of response under the hypothesis of dose additivity is equal to a given constant, the associated optimal design measures have three support points; these designs may also be implemented for the purpose of conducting nonparametric tests for synergy at specified dose combinations. Given that one is allowed to choose from dose combinations located within a general two-dimensional dose space, the associated optimal design measures are typically constructed using iterative procedures. Bayesian and minimax approaches to optimal design construction are also considered, and methods are proposed by which one can construct designs which are robust to possible misspecifications of the initial estimates of the parameters in the associated dose-response model.

ISBN: 0612538737Subjects--Topical Terms:

517247
Statistics.

Optimal designs for the detection of drug interaction.
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100 1 $a Kupchak, Petro Iwan. $3 1949309
245 1 0 $a Optimal designs for the detection of drug interaction.
300 $a 179 p.
500 $a Source: Dissertation Abstracts International, Volume: 61-11, Section: B, page: 5948.
500 $a Adviser: Nancy Reid.
502 $a Thesis (Ph.D.)--University of Toronto (Canada), 2000.
520 $a Methods of statistical inference are developed which can be used to test the hypothesis of dose additivity at a specified dose combination, given that one has access to binary dose-response data collected from an experiment where two drugs are administered in varying doses to a group of subjects. The distributional properties of the estimate of the interaction parameter in a bivariate logistic dose-response model are also investigated, via simulation studies. Using the principles of optimal design theory for nonlinear dose-response models, design measures are constructed which minimize various criteria related to the precision with which one can quantify the degree of interaction between two drugs. Given that one is allowed to choose from dose combinations such that the associated probability of response under the hypothesis of dose additivity is equal to a given constant, the associated optimal design measures have three support points; these designs may also be implemented for the purpose of conducting nonparametric tests for synergy at specified dose combinations. Given that one is allowed to choose from dose combinations located within a general two-dimensional dose space, the associated optimal design measures are typically constructed using iterative procedures. Bayesian and minimax approaches to optimal design construction are also considered, and methods are proposed by which one can construct designs which are robust to possible misspecifications of the initial estimates of the parameters in the associated dose-response model.
590 $a School code: 0779.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
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710 2 0 $a University of Toronto (Canada). $3 1017674
773 0 $t Dissertation Abstracts International $g 61-11B.
790 1 0 $a Reid, Nancy, $e advisor
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791 $a Ph.D.
792 $a 2000
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ53873