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You searched for subject:(latent covariate). Showing records 1 – 2 of 2 total matches.

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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 07, 2020. http://hdl.handle.net/10523/4520.

MLA Handbook (7th Edition):

Lagishetty, Chakradhar. “Covariates in Pharmacometrics .” 2013. Web. 07 Aug 2020.

Vancouver:

Lagishetty C. Covariates in Pharmacometrics . [Internet] [Doctoral dissertation]. University of Otago; 2013. [cited 2020 Aug 07]. 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


University of South Florida

2. Wang, Yan. Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups.

Degree: 2018, University of South Florida

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This Monte Carlo simulation study examined the issue of measurement invariance testing with FMM when there are covariate effects. Specifically, this study investigated the impact of excluding and misspecifying covariate effects on the class enumeration and measurement invariance testing with FMM. Data were generated based on three FMM models where the covariate had impact on the latent class membership only (population model 1), both the latent class membership and the factor (population model 2), and the latent class membership, the factor, and one item (population model 3). The number of latent classes was fixed at two. These two latent classes were distinguished by factor mean difference for conditions where measurement invariance held in the population, and by both factor mean difference and intercept differences across classes when measurement invariance did not hold in the population. For each of the population models, different analysis models that excluded or misspecified covariate effects were fitted to data. Analyses consisted of two parts. First, for each analysis model, class enumeration rates were examined by comparing the fit of seven solutions: 1-class, 2-class configural, metric, and scalar, and 3-class configural, metric, and scalar. Second, assuming the correct solution was selected, the fit of analysis models with the same solution was compared to identify a best-fitting model. Results showed that completely excluding the covariate from the model (i.e., the unconditional model) would lead to under-extraction of latent classes, except when the class separation was large. Therefore, it is recommended to include covariate in FMM when the focus is to identify the number of latent classes and the level of invariance. Specifically, the covariate effect on the latent class membership can be included if there is no priori hypothesis regarding whether measurement invariance might hold or not. Then fit of this model can be compared with other models that included covariate effects in different ways but with the same number of latent classes and the same level of invariance to identify a best-fitting model.

Subjects/Keywords: class enumeration; covariate effect; latent classes; measurement invariance; Educational Assessment, Evaluation, and Research

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

APA (6th Edition):

Wang, Y. (2018). Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/7715

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Yan. “Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups.” 2018. Thesis, University of South Florida. Accessed August 07, 2020. https://scholarcommons.usf.edu/etd/7715.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Yan. “Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups.” 2018. Web. 07 Aug 2020.

Vancouver:

Wang Y. Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups. [Internet] [Thesis]. University of South Florida; 2018. [cited 2020 Aug 07]. Available from: https://scholarcommons.usf.edu/etd/7715.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups. [Thesis]. University of South Florida; 2018. Available from: https://scholarcommons.usf.edu/etd/7715

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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