| dc.description.abstract | Real world observational pharmacokinetic (PK) data, e.g., from Electronic Health Records or measurements collected during routine care, require additional analysis considerations relative to traditional PK trials. These data are more likely to be sparse, imbalanced, or error-prone, and the practical limitations must be considered to implement methods in a clinical setting. In this dissertation, we developed and investigated methodologies to improve studies conducted using real world PK data. First, we developed a natural language processing algorithm medExtractR which targets the extraction of dose information from free-text clinical notes for a specific medication of interest. Contrasted with existing general-purpose medication extraction algorithms, medExtractR was able to achieve better entity-level extraction, and maintained high performance on an external validation set with tuning and customization. Next, we examined an approximate Bayesian model for individualized PK estimation via a simulation study with opportunistic concentration measurements. The intended for therapeutic drug monitoring necessitated approximation methods that were both accurate and computationally efficient. We evaluated the accuracy of uncertainty estimates and implemented the methods into easy-to-use web interface. Finally, we addressed the issue of bias in estimation as a result of time recording errors (TREs) in PK data by evaluating various modifications to study design and data collection. We considered pragmatic strategies minimally invasive to a clinical workflow that could be implemented in an opportunistic data setting. Mitigation strategies included delaying timing of blood draws to non-infusion periods, selecting future draw times based on minimizing bias from simulated errors, and identifying patients whose existing measurements were most sensitive to TREs. For an intravenously infused drug, we found that these strategies reduced bias in estimation of a pharmacodynamic endpoint more for dosing schedules with rapid infusions than those with slower infusions. | |
