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Causal Inference in Pharmacoepidemiology

In: Quantitative Methods in Pharmaceutical Research and Development

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ashley Buchanan & Tianyu Sun & Natallia V. Katenka, 2020. "Causal Inference in Pharmacoepidemiology," Springer Books, in: Olga V. Marchenko & Natallia V. Katenka (ed.), Quantitative Methods in Pharmaceutical Research and Development, chapter 0, pages 181-224, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-48555-9_5
    DOI: 10.1007/978-3-030-48555-9_5
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