Skip to main content
Log in

Emulating a Target Trial in Perinatal Pharmacoepidemiology: Challenges and Methodological Approaches

  • Published:
Current Epidemiology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

The objective of this review is to examine the application of target trial emulation in perinatal pharmacoepidemiology research. Given that randomized clinical trials—the gold standard for causal inference—are often not feasible or ethical for studying medication safety during pregnancy, alternative methodologies are critically needed. This paper delves into the challenges and potential mitigation strategies of using target trial emulation in the specific context of perinatal pharmacoepidemiology research.

Recent Findings

Our review of identified studies (n = 9) reveals several unique considerations when leveraging target trial emulation for perinatal pharmacoepidemiology research. These include the alignment of the research question with the clinically relevant outcomes, identification of etiologically relevant time windows, defining relevant treatment strategies, and anchoring of exposure, eligibility criteria, and the start of follow-up. Despite these challenges, the methodology shows promise in bridging the gap between randomized clinical trials and observational research through the employment of a transparent and well-defined approach.

Summary

Target trial emulation serves as a valuable tool in perinatal pharmacoepidemiology, allowing researchers to generate more reliable evidence concerning medication safety during pregnancy. Although the approach comes with specific challenges, strategies can be implemented to mitigate these difficulties. Overall, the adoption of target trial emulation has the potential to substantially enhance evidence quality, inform clinical decisions, and ultimately improve health outcomes for birthing people and their infants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

Not applicable.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Hariton E, Locascio JJ. Randomised controlled trials—the gold standard for effectiveness research. BJOG. 2018;125(13):1716.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Caniglia EC, et al. Emulating target trials to avoid immortal time bias–an application to antibiotic initiation and preterm delivery. Epidemiology. 2023;34(3):430–8. This study describes how to emulate a sequence of target trials to avoid immortal time bias, and applies the approach to estimate the safety of antibiotic initiation between 24 and 37 weeks gestation on preterm delivery.

    Article  PubMed  Google Scholar 

  3. Food and Drug Administration (FDA), Pregnant women: scientific and ethical considerations for inclusion in clinical trials guidance for industry. Draft guidance, 2018.

  4. Leal LF, et al. The use of the target trial approach in perinatal pharmacoepidemiology: a scoping review protocol. Front Pharmacol. 2022;13:904824.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hernández-Díaz S, et al. Emulating a target trial of interventions initiated during pregnancy with healthcare databases: the example of COVID-19 vaccination. Epidemiology. 34(2):238–46. This study utilizes the target trial framework to provide a step-by-step description of how to use healthcare databases to estimate the effects of interventions initiated during pregnancy.

  6. Huybrechts KF, Bateman BT, Hernández-Díaz S. Use of real-world evidence from healthcare utilization data to evaluate drug safety during pregnancy. Pharmacoepidemiol Drug Saf. 2019;28(7):906–22. This paper describes the distinctive methodological aspects of conducting drug safety studies in healthcare utilization databases with special emphasis on design and analytic approaches to minimize biases.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wood ME, et al. Making fair comparisons in pregnancy medication safety studies: an overview of advanced methods for confounding control. Pharmacoepidemiol Drug Saf. 2018;27(2):140–7.

    Article  PubMed  Google Scholar 

  8. Ukah UV, et al. Time-related biases in perinatal pharmacoepidemiology: a systematic review of observational studies. Pharmacoepidemiol Drug Saf. 2022;31(12):1228–41.

    Article  PubMed  Google Scholar 

  9. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–64.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hernán MA, et al. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70–5.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wood, M.E., C.D. Latour, and L.C. Petito, Treatments for pregestational chronic conditions during pregnancy: emulating a target trial with a treatment decision design. https://arxiv.org/abs/2305.13540, 2023.

  12. Fell DB, et al. Guidance for design and analysis of observational studies of fetal and newborn outcomes following COVID-19 vaccination during pregnancy. Vaccine. 2021;39(14):1882–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chiu Y-H, et al. Effectiveness and safety of intrauterine insemination vs. assisted reproductive technology: emulating a target trial using an observational database of administrative claims. Fertil Steril. 2022;117(5):981–91.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Meyer A, et al. Benefits and risks associated with continuation of anti–tumor necrosis factor after 24 weeks of pregnancy in women with inflammatory bowel disease: a nationwide emulation trial. Ann Intern Med. 2022;175(10):1374–82.

    Article  PubMed  Google Scholar 

  15. Caniglia EC, et al. Emulating a target trial of antiretroviral therapy regimens started before conception and risk of adverse birth outcomes. AIDS. 2018;32(1):113.

    Article  PubMed  Google Scholar 

  16. Yland JJ, et al. Emulating a target trial of the comparative effectiveness of clomiphene citrate and letrozole for ovulation induction. Hum Reprod. 2022;37(4):793–805.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schnitzer ME, et al. A potential outcomes approach to defining and estimating gestational age-specific exposure effects during pregnancy. Stat Methods Med Res. 2022;31(2):300–14.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Dehaene I, et al. Relevance of the antenatal corticosteroids-to-delivery interval in the prevention of neonatal respiratory distress syndrome through the eyes of causal inference: a review and target trial. Arch Gynecol Obstet. 2022;305(4):885–92.

    Article  CAS  PubMed  Google Scholar 

  19. Goetghebeur E, et al. Formulating causal questions and principled statistical answers. Stat Med. 2020;39(30):4922–48.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Edwards JK, Htoo PT, Stürmer T. Counterpoint: Keeping the demons at bay when handling time-varying exposures-beyond avoiding immortal person-time. Am J Epidemiol. 2019;188(6):1016–22.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Howe CJ, et al. Limitation of inverse probability-of-censoring weights in estimating survival in the presence of strong selection bias. Am J Epidemiol. 2011;173(5):569–77.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Starfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. Milbank Q. 2005;83(3):457–502.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lesko CR, et al. Target validity: bringing treatment of external validity in line with internal validity. Curr Epidemiol Rep. 2020;7(3):117–24.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yola M, Lucien A. Evidence of the depletion of susceptibles effect in non-experimental pharmacoepidemiologic research. J Clin Epidemiol. 1994;47(7):731–7.

    Article  Google Scholar 

  25. Brookhart MA. Counterpoint: the treatment decision design. Am J Epidemiol. 2015;182(10):840–5.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lévesque LE, et al. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. Bmj. 2010:340.

  27. Moller A-B, et al. Early antenatal care visit: a systematic analysis of regional and global levels and trends of coverage from 1990 to 2013. Lancet Glob Health. 2017;5(10):e977–83.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Osterman, M.J. and J.A. Martin, SystemTiming and adequacy of prenatal care in the United States, 2016. 2018.

    Google Scholar 

  29. Lupattelli A, Spigset O, Nordeng H. Adherence to medication for chronic disorders during pregnancy: results from a multinational study. Int J Clin Pharmacol. 2014;36(1):145–53.

    Article  Google Scholar 

  30. Adhikari K, et al. Adherence to and persistence with antidepressant medication during pregnancy: does it differ by the class of antidepressant medication prescribed? Can J Psychiatry. 2019;64(3):199–208.

    Article  PubMed  Google Scholar 

  31. Helou A, Stewart K, George J. Adherence to anti-hypertensive medication in pregnancy. Pregnancy Hypertens. 2021;25:230–4.

    Article  PubMed  Google Scholar 

  32. Watanabe C, et al. Non-adherence to medications in pregnant ulcerative colitis patients contributes to disease flares and adverse pregnancy outcomes. Dig Dis Sci. 2021;66(2):577–86.

    Article  PubMed  Google Scholar 

  33. Chakraborty B, Murphy SA. Dynamic treatment regimes. Annu Rev Stat Appl. 2014;1:447–64.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Young JG, et al. A causal framework for classical statistical estimands in failure-time settings with competing events. Stat Med. 2020;39(8):1199–236.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hernán MA, Schisterman EF, Hernández-Díaz S. Invited commentary: composite outcomes as an attempt to escape from selection bias and related paradoxes. Am J Epidemiol. 2014;179(3):368–70.

    Article  PubMed  Google Scholar 

  36. Joseph K, Kramer MS. The fetuses-at-risk approach: survival analysis from a fetal perspective. Acta Obstet Gynecol Scand. 2018;97(4):454–65.

    Article  CAS  PubMed  Google Scholar 

  37. Kramer MS, Zhang X, Platt RW. Analyzing risks of adverse pregnancy outcomes. Am J Epidemiol. 2014;179(3):361–7.

    Article  PubMed  Google Scholar 

  38. Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337–46.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Karim ME, Pang M, Platt RW. Can we train machine learning methods to outperform the high-dimensional propensity score algorithm? Epidemiology. 2018;29(2):191–8.

    Article  PubMed  Google Scholar 

  40. Wyss R, et al. Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: an overview of the current literature. Pharmacoepidemiol Drug Saf. 2022;31(9):932–43.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wyss R, et al. Using super learner prediction modeling to improve high-dimensional propensity score estimation. Epidemiology. 2018;29(1):96–106.

    Article  PubMed  Google Scholar 

  42. Arain Z, et al. Machine learning and disease prediction in obstetrics. Curr Res Physiol. 2023;6:100099.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Byrne JJ, Saucedo AM, Spong CY. Task force on research specific to pregnant and lactating women. In: Seminars in Perinatology. Elsevier; 2020.

    Google Scholar 

  44. Torgersen KL, Curran CA. A systematic approach to the physiologic adaptations of pregnancy. Crit Care Nurs Q. 2006;29(1):2–19.

    Article  PubMed  Google Scholar 

  45. Zhao Y, Hebert MF, Venkataramanan R. Basic obstetric pharmacology. Semin Perinatol. 2014;38(8):475–86.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

S.C. and S.M.G. conceived the study idea in consultation with all other co-authors. S.C, L.T., and S.M.G. drafted the manuscript, and all other authors (R.W.P. and M.E.W.) reviewed the manuscript for intellectual content and approved the submitted manuscript.

Corresponding author

Correspondence to Sonia M. Grandi.

Ethics declarations

Conflict of Interest

MW is a member of the Center for Pharmacoepidemiology (CPE) at UNC, which receives funding from industry partners (AbbVie, Boehringer Ingleheim, GSK, Takeda, UCB, Sarepta, Astellas). They do not receive salary support from the CPE; funds are used to support student stipends and related expenses. They received a starter grant from the PhRMA Foundation to study the treatment of chronic hypertension in pregnancy. They are a co-I on grants from the CDC, NIH, and the Kuni Foundation, unrelated to the current work.

RWP holds the Albert Boehringer I Chair in Pharmacoepidemiology and has received personal fees from Amgen, Analysis Group, Biogen, Boehringer Ingelheim, Merck, Nant Pharma, and Pfizer, all outside of the submitted work.

SMG holds a grant as nominated principal investigator from the Canadian Institutes of Health Research (CIHR) and is a co-I on grants from the CIHR, University of Toronto Data Sciences Institute, and the Ontario Ministry of Health, unrelated to this work. She also received start-up funds from the Hospital for Sick Children, unrelated to this work.

All other authors have no conflicts of interest to disclose.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 23.3 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chiodo, S., Tailor, L., Platt, R.W. et al. Emulating a Target Trial in Perinatal Pharmacoepidemiology: Challenges and Methodological Approaches. Curr Epidemiol Rep 10, 275–285 (2023). https://doi.org/10.1007/s40471-023-00339-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40471-023-00339-7

Keywords

Navigation