Display options
Cite
Wood ME, Lupattelli A, Palmsten K, et al. Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy. Epidemiol Rev. 2021;doi: 10.1093/epirev/mxab002.
Wood, M. E., Lupattelli, A., Palmsten, K., Bandoli, G., Hurault-Delarue, C., Damase-Michel, C., Chambers, C. D., Nordeng, H. M. E., & van Gelder, M. M. H. J. (2021). Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy. Epidemiologic reviews, . https://doi.org/10.1093/epirev/mxab002
Wood, Mollie E, et al. "Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy." Epidemiologic reviews vol. (2021). doi: https://doi.org/10.1093/epirev/mxab002
Wood ME, Lupattelli A, Palmsten K, Bandoli G, Hurault-Delarue C, Damase-Michel C, Chambers CD, Nordeng HME, van Gelder MMHJ. Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy. Epidemiol Rev. 2021 Jun 08; doi: 10.1093/epirev/mxab002. Epub 2021 Jun 08. PMID: 34100086.
Copy Download .nbib Format: NLM
Share it on

Epidemiol Rev. 2021 Jun 08; doi: 10.1093/epirev/mxab002. Epub 2021 Jun 08.

Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy.

Epidemiologic reviews

Mollie E Wood, Angela Lupattelli, Kristin Palmsten, Gretchen Bandoli, Caroline Hurault-Delarue, Christine Damase-Michel, Christina D Chambers, Hedvig M E Nordeng, Marleen M H J van Gelder

Affiliations

  1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States.
  2. PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  3. HealthPartners Institute, Minneapolis, Minnesota, United States.
  4. Department of Pediatrics, University of California, San Diego, La Jolla, California, United States.
  5. Herbert Wertheim School of Public Health, University of California, San Diego, La Jolla, California, United States.
  6. Service de Pharmacologie Médicale, Faculté de Médecine, Université Toulouse III, Centre Hospitalier Universitaire, UMR INSERM 1295, Toulouse, France.
  7. Department of Child Development and Health, Norwegian Institute of Public Health, Oslo, Norway.
  8. Department for Health Evidence, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, The Netherlands.
  9. Radboud REshape Innovation Center, Radboud university medical center, Nijmegen, The Netherlands.

PMID: 34100086 DOI: 10.1093/epirev/mxab002

Abstract

In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as "ever exposed" versus "never exposed" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and fails to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex individual level medication utilization trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods including k-means clustering, group-based trajectory models, and hierarchical cluster analysis are of interest as they allow for visual examination of medication utilization trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into co-medication and drug switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and g-methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.

© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Keywords: Clustering methods; Confounding factors (Epidemiology); Cox Models; Epidemiologic methods; Longitudinal studies; Medication; Pregnancy; Time-varying exposure methods

Publication Types

Grant support