Quantitative Methods for Evidence Building in Clinical Pharmacology and Pharmaceutical Outcomes Research
2021-05
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Quantitative Methods for Evidence Building in Clinical Pharmacology and Pharmaceutical Outcomes Research
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2021-05
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A variety of methods are employed to build evidence in pharmacology and pharmaceutical outcomes research. Descriptive and inferential statistics are used to describe the data and generalize findings to populations. Regression models, propensity score adjustment, and meta-analysis extend upon the quantitative approach to building evidence. Topic areas in this dissertation include demonstrating the application of these methods to a comparison of three-factor prothrombin complex concentrate versus four-factor prothrombin complex concentrate for emergent warfarin reversal via a propensity score adjusted retrospective cohort study and a systematic review and meta-analysis to address clinical problems and improve health outcomes. Also presented are the pharmacokinetics of intravenous N-acetylcysteine, Cysteine, and Glutathione and the effect of N-acetylcysteine as a reducing agent in Parkinson’s disease and Gaucher disease. While quantitative methods help us explore, explain, and generalize from data, it is imperative to consider the clinical relevance of the findings. We found that four-factor prothrombin complex concentrate is preferred for emergent warfarin reversal. This is a finding is useful in real-world patient care. Also, increased N-acetylcysteine plasma concentrations and Glutathione redox ratio are related, which could be used to optimize dosing in future studies. These examples are described in detail as examples of applications of quantitative methods.
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University of Minnesota Ph.D. dissertation. May 2021. Major: Experimental & Clinical Pharmacology. Advisors: Scott Chapman, Mark Kirstein. 1 computer file (PDF); xi, 168 pages.
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Margraf, David. (2021). Quantitative Methods for Evidence Building in Clinical Pharmacology and Pharmaceutical Outcomes Research. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/257103.
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