Causal Inference in Pharmacoepidemiology

Ashley Buchanan (), Tianyu Sun and Natallia V. Katenka
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Ashley Buchanan: University of Rhode Island, College of Pharmacy, Department of Pharmacy Practice
Tianyu Sun: University of Rhode Island, College of Pharmacy, Department of Pharmacy Practice
Natallia V. Katenka: University of Rhode Island, Department of Computer Science and Statistics

Chapter Chapter 5 in Quantitative Methods in Pharmaceutical Research and Development, 2020, pp 181-224 from Springer

Abstract: Abstract The causal inference framework can be employed to quantify causal effects in both randomized and non-randomized settings. Often in pharmacoepidemiology research, study designs lack randomized interventions that allow for causal inference. Yet, there are important and meaningful causal questions to address for non-randomized interventions. In this chapter, we provide an overview of the causal inference paradigm, review current methodology, and discuss applications of these concepts to strengthen and improve pharmacoepidemiologic studies. Specifically, we introduce marginal structural models fit using inverse probability weights and discuss additional advanced topics, including survival analyses, time-varying exposure, and instrumental variables. We conclude the chapter with references to relevant software packages.

Keywords: Big health outcomes databases; Causal inference; Observational studies; Pharmacoepidemiology; Randomized studies (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-48555-9_5

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