{"id":{"repo_id":"greenwich","oai_identifier":"oai:gala.gre.ac.uk:8654"},"canonical_url":"https://search.dev.ndltd.org/etd/greenwich/oai:gala.gre.ac.uk:8654","repository":{"repo_id":"greenwich","name":"University of Greenwich","base_url":"https://gala.gre.ac.uk/cgi/oai2"},"display":{"title":"Population pharmacokinetics: model-free approach and nonlinear mixed-effects modelling","abstract":"The work is devoted to the application and further development of modern statistical methods to study pharmacokinetics of drugs. Specifically, it deals with applications and development of repeated measures analysis, so called 'population approach' methods, in the field of pharmacokinetics. hi the first part of the thesis, a new, model-free approach is developed and tested. It introduces a model-free measure of patient's exposure to drugs, and then investigates the relationships between the exposure level and covariates using various statistical techniques. Classification tree models (CART) and regression analysis are used to study various subpopulations of interest. It is shown, via simulations, that the model-free method is capable to identify predictors of exposure in a wide range of variability in the data. The non-linear mixed effect modelling is used to confirm the results of the model-free investigation. Model-free approach is successfully applied to several drugs. Non-linear Mixed Effects population models developed for the same data agree with its results. Limits of the new method are also identified. Specifically, it does not allow the estimation of the variability: either the within-subject (intra-individual) variability in response, or between-subject (inter-individual) variability of the pharmacokinetic parameters in the population. The second part of the thesis is devoted to applications of the Non-linear Mixed Effect methodology to population pharmacokinetics and dose-response analysis. Population pharmacokinetic and dose-response models of several drugs are developed. Pharmacokinetic models allow for complete characterisation of the drug's pharmacokinetics and its relationships to safety and efficacy. The developed models are used to explore the relationships between the exposure (individual Bayes estimates) and demographic predictors of exposure, and safety and efficacy of the drug. Finally, the developed models are used in simulations to guide the design of new studies.","abstract_html":"The work is devoted to the application and further development of modern statistical methods to study pharmacokinetics of drugs. Specifically, it deals with applications and development of repeated measures analysis, so called 'population approach' methods, in the field of pharmacokinetics. hi the first part of the thesis, a new, model-free approach is developed and tested. It introduces a model-free measure of patient's exposure to drugs, and then investigates the relationships between the exposure level and covariates using various statistical techniques. Classification tree models (CART) and regression analysis are used to study various subpopulations of interest. It is shown, via simulations, that the model-free method is capable to identify predictors of exposure in a wide range of variability in the data. The non-linear mixed effect modelling is used to confirm the results of the model-free investigation. Model-free approach is successfully applied to several drugs. Non-linear Mixed Effects population models developed for the same data agree with its results. Limits of the new method are also identified. Specifically, it does not allow the estimation of the variability: either the within-subject (intra-individual) variability in response, or between-subject (inter-individual) variability of the pharmacokinetic parameters in the population. The second part of the thesis is devoted to applications of the Non-linear Mixed Effect methodology to population pharmacokinetics and dose-response analysis. Population pharmacokinetic and dose-response models of several drugs are developed. Pharmacokinetic models allow for complete characterisation of the drug's pharmacokinetics and its relationships to safety and efficacy. The developed models are used to explore the relationships between the exposure (individual Bayes estimates) and demographic predictors of exposure, and safety and efficacy of the drug. Finally, the developed models are used in simulations to guide the design of new studies.","abstract_has_math":false,"creators":["Gibiansky, Ekaterina"],"institution":"University of Greenwich","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Rennolls, Keith"],"committee_chairs":[],"committee_members":[],"year":1999,"date_issued":"1999-11","date_published":"1999-11","updated_at":"2026-07-24T02:25:45Z","subjects":["QA Mathematics"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rennolls, Keith"]},{"key":"dc:creator","label":"Author","values":["Gibiansky, Ekaterina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["1999-11"]},{"key":"dc:date.issued","label":"Date","values":["1999-11"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Computing and Mathematical Sciences"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Greenwich"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://gala.gre.ac.uk/id/eprint/8654/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["QA Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://gala.gre.ac.uk/id/eprint/8654/1/Ekaterina_Gibiansky_1999.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The work is devoted to the application and further development of modern statistical methods to study pharmacokinetics of drugs. Specifically, it deals with applications and development of repeated measures analysis, so called 'population approach' methods, in the field of pharmacokinetics. hi the first part of the thesis, a new, model-free approach is developed and tested. It introduces a model-free measure of patient's exposure to drugs, and then investigates the relationships between the exposure level and covariates using various statistical techniques. Classification tree models (CART) and regression analysis are used to study various subpopulations of interest. It is shown, via simulations, that the model-free method is capable to identify predictors of exposure in a wide range of variability in the data. The non-linear mixed effect modelling is used to confirm the results of the model-free investigation. Model-free approach is successfully applied to several drugs. Non-linear Mixed Effects population models developed for the same data agree with its results. Limits of the new method are also identified. Specifically, it does not allow the estimation of the variability: either the within-subject (intra-individual) variability in response, or between-subject (inter-individual) variability of the pharmacokinetic parameters in the population. The second part of the thesis is devoted to applications of the Non-linear Mixed Effect methodology to population pharmacokinetics and dose-response analysis. Population pharmacokinetic and dose-response models of several drugs are developed. Pharmacokinetic models allow for complete characterisation of the drug's pharmacokinetics and its relationships to safety and efficacy. The developed models are used to explore the relationships between the exposure (individual Bayes estimates) and demographic predictors of exposure, and safety and efficacy of the drug. Finally, the developed models are used in simulations to guide the design of new studies."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Population pharmacokinetics: model-free approach and nonlinear mixed-effects modelling"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rennolls, Keith"],"dc:creator":["Gibiansky, Ekaterina"],"dc:date":["1999-11"],"dc:date.issued":["1999-11"],"dc:description.abstract":["The work is devoted to the application and further development of modern statistical methods to study pharmacokinetics of drugs. Specifically, it deals with applications and development of repeated measures analysis, so called 'population approach' methods, in the field of pharmacokinetics. hi the first part of the thesis, a new, model-free approach is developed and tested. It introduces a model-free measure of patient's exposure to drugs, and then investigates the relationships between the exposure level and covariates using various statistical techniques. Classification tree models (CART) and regression analysis are used to study various subpopulations of interest. It is shown, via simulations, that the model-free method is capable to identify predictors of exposure in a wide range of variability in the data. The non-linear mixed effect modelling is used to confirm the results of the model-free investigation. Model-free approach is successfully applied to several drugs. Non-linear Mixed Effects population models developed for the same data agree with its results. Limits of the new method are also identified. Specifically, it does not allow the estimation of the variability: either the within-subject (intra-individual) variability in response, or between-subject (inter-individual) variability of the pharmacokinetic parameters in the population. The second part of the thesis is devoted to applications of the Non-linear Mixed Effect methodology to population pharmacokinetics and dose-response analysis. Population pharmacokinetic and dose-response models of several drugs are developed. Pharmacokinetic models allow for complete characterisation of the drug's pharmacokinetics and its relationships to safety and efficacy. The developed models are used to explore the relationships between the exposure (individual Bayes estimates) and demographic predictors of exposure, and safety and efficacy of the drug. 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