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Title: Statistical models for pharmaceutical extremes
Author: Papastathopoulos, Ioannis
ISNI:       0000 0004 5362 276X
Awarding Body: Lancaster University
Current Institution: Lancaster University
Date of Award: 2015
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Drug toxicity is usually triggered by the occurrence of a combination of extreme values of laboratory variables that are collected in clinical trials. Drug-induced liver injury (DILI) has been the most frequently related single cause of safety-related drug marketing withdrawals for the past 50 years. The importance of assessing the safety of a drug is illustrated through its pre-marketing evaluation. Safety testing is ubiquitous in all phases of clinical trials and early detection of toxicity is key to preventing severe adverse events as well as to reducing the huge financial cost due to the long-term pre-marketing screening of a new drug. The current applied and methodological interest is in univariate and multivariate extreme value models that are typically fitted to a fraction of the data and form the basis of all subsequent predictions and inferential aspects for the problem under study. Typical challenges that arise in pharmaceutical applications among others are the limited source of information, commonly measured by the sample size, and the accurate estimation of the underlying dependence structure These challenges motivate the present thesis which focuses on constructing and improving extreme value models that have direct potential application to the pharmaceutical industry. In particular, in this thesis we focus on i. providing alternatives to univariate extreme value threshold models that can be fitted to lower thresholds and improve the stability of. the parameter estimates as well as the efficiency of the estimators; ii. introducing additional constraints for, and slight changes in, the model formulation and parameter space of two commonly used multivariate extreme value approaches, namely the conditional extremal dependence model and the component-wise maxima approach. These changes in the method are aimed to overcome complications that have been experienced with using these models in terms of modelling negatively associated random variables, overcoming identifiability problems, and to avoid drawing invalid inferences; 111. extending the conditional extremal dependence model to incorporate subject specific knowledge and a natural ordering between doses in the estimation of the probability of DILI; IV. exploring techniques from multivariate analysis and constructing diagnostic measures for estimating the graphical structure of extreme multivariate events.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available