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University of Otago

1. Lagishetty, Chakradhar. Covariates in Pharmacometrics .

Degree: 2013, University of Otago

Understanding the variability in drug response forms an important aspect of pharmacometrics. Various biological, statistical, clinical and mathematical concepts need to be considered to reach a unified decision point to understand and quantify sources of variability. This current work involves studies on methodological and clinical exploratory evaluation of covariates in the context of pharmacometrics. Studies have been conducted using theoretic approaches on the design of pharmacokinetic (PK) studies for latent covariates, use of a reduction in random between subject variability as a covariate selection criterion and evaluated methods to handle non-ignorable nuisance covariates. Exploratory studies were also conducted in a clinical & experimental framework for identification of suitable metrics of organ function as covariates to predict drug clearance. Part I of this thesis includes methodological evaluation of covariates with Chapters 2, 3 and 4. Part II involves clinical exploratory evaluation of covariates with Chapters 5 and 6. Chapter 2 involved studies on the design of pharmacokinetic studies for latent covariates. The motivating context for this work was from a single nucleotide polymorphism (SNP) believed to influence clearance. This led to exploration of the concept of latent covariates which can have uncertainty in both their distribution and frequency. Simulation studies were conducted in both linear regression and nonlinear mixed effects modelling (NLMEM) frameworks assuming both even and uneven frequencies of the covariate. The designs for latent covariates were evaluated assuming continuous, ordinal and nominal distribution of covariates. Initially, the designs were evaluated in a theoretic framework using linear regression. Then, these were evaluated in a NLMEM framework assuming direct influence of latent covariate or indirect influence of latent covariate via another observable continuous covariate on parameter of interest. It was observed that continuous models performed better than categorical models. A covariate selection criterion was evaluated in Chapter 3. In pharmacometric analysis, a reduction in random between subject variability is used as part of standard criteria for selection of a covariate. The covariate is not selected if it failed to reduce random between subject variance (BSVR) in the model. Studies were conducted in a simulation framework to assess nested covariate models (NCM) and not nested covariate models (NNCM). Further, covariate-η interaction models were explored but were found to be marginally important. NCMs were found to be more robust to model misspecification than NNCMs which may not result in a reduction in BSVR. Chapter 4 explores analysis methods for handling nuisance covariates. The frequency with which a covariate occurs is important when interpreting its effect size. Covariates like genotypes and concomitant medication are sometimes present at low frequencies or as rare events. Due to alpha error inflation, estimates of their effect size may be false. These are… Advisors/Committee Members: Duffull, Stephen (advisor).

Subjects/Keywords: latent covariate; between subject variability; nuisance covariate; telomere length; single nucleotide polymorphisms; qPCR assay; genotyping; biological age; organ function; Pharmacometrics analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lagishetty, C. (2013). Covariates in Pharmacometrics . (Doctoral Dissertation). University of Otago. Retrieved from http://hdl.handle.net/10523/4520

Chicago Manual of Style (16th Edition):

Lagishetty, Chakradhar. “Covariates in Pharmacometrics .” 2013. Doctoral Dissertation, University of Otago. Accessed August 06, 2020. http://hdl.handle.net/10523/4520.

MLA Handbook (7th Edition):

Lagishetty, Chakradhar. “Covariates in Pharmacometrics .” 2013. Web. 06 Aug 2020.

Vancouver:

Lagishetty C. Covariates in Pharmacometrics . [Internet] [Doctoral dissertation]. University of Otago; 2013. [cited 2020 Aug 06]. Available from: http://hdl.handle.net/10523/4520.

Council of Science Editors:

Lagishetty C. Covariates in Pharmacometrics . [Doctoral Dissertation]. University of Otago; 2013. Available from: http://hdl.handle.net/10523/4520

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