EB Research: Causal Inference in Reproductive and Perinatal Epidemiology

This study extends the methodological framework for causal inference to reproductive and perinatal epidemiology. The objective of this research is to develop methods using causal inference tools, specifically as they improve researchers' understanding of confounding and colliders, and as applied to the Birth Weight Paradox and the role of birth weight in analysis of perinatal data. In addition, the work aims to apply the same tools to better understand the role of history of prior outcomes in appropriate modeling. Our team of researchers has made significant contributions to this literature in the following areas:

  • Birth Weight Paradox: The team used analytical methods as well as directed acyclic graphs (DAGs) to graphically evaluate bias, confounding, and possible explanations for the birth weight paradox. In addition, a combination of DAGs and simulation studies were utilized to quantify bias and evaluate the proposed solution of utilizing birth weight z-scores.
     
  • Overadjustment: Using causal diagrams, analytical, proofs, and an empirical example estimating the total effect of maternal smoking on neonatal mortality, researchers at NICHD illustrated and clarified the definition of overadjustment bias, distinguished overadjustment bias from unwarranted adjustment, and quantified the amount of bias and loss of precision associated with overadjustment and unwarranted adjustment.
     
  • Role of Prior Outcomes: Pregnancy outcomes, such as spontaneous abortion and preterm birth, are often predictive of future pregnancy outcomes. As a result, many researchers adjust for reproductive history. Research using DAGs illustrates that this may not always be the correct approach. In fact, there is no single answer as to whether reproductive history should be included in the model; the decision depends on the research question and the underlying DAG.
     
  • Exposure-Enriched Designs: Designs of a cohort study can be enriched for a primary exposure of interest to improve cost-effectiveness. We have shown that caution should be employed when conducting secondary analyses in studies that have already been enriched, intentionally or unintentionally, for a primary exposure of interest.  Specifically, causal diagrams can help identify scenarios in which secondary analyses may be biased, and specific analytical methods can be used to remove the bias (e.g., inverse probability weights).
     
  • Person-Time At Risk: Although commonly excluded, we have shown that there are scenarios in which person-time not at risk should be included.  When interested in estimating treatment effects that allow and account for potential noncompliance, or where the exposure may be associated with the time at risk, we argue that person-time not at risk should be included.  In the case of time-to-pregnancy, although the intention-to-treat (ITT)-type analysis may underestimate the biological fecundity of the population, it may also yield an answer to a question that is of more interest to couples trying to become pregnant.

Principal Investigator

Enrique F. Schisterman, Ph.D.

DIPHR Collaborators

Selected Publications

  • Perkins NJ, Cole SR, Harel O, Tchetgen Tchetgen EJ, Sun B, Mitchell EM, Schisterman EF. Principled Approaches to Missing Data in Epidemiologic Studies. American Journal of Epidemiology. 2018; 187(3):568-575. PMID: 29165572. PMCID: PMC5860376
  • Sun B, Perkins NJ, Cole SR, Harel O, Mitchell EM, Schisterman EF, Tchetgen Tchetgen EJ. Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data. American Journal of Epidemiology. 2018; 187(3):585-591. PMID: 29165557. PMCID: PMC5860553
  • Harel O, Mitchell EM, Perkins NJ, Cole SR, Tchetgen Tchetgen EJ, Sun B, Schisterman EF. Multiple Imputation for Incomplete Data in Epidemiologic Studies. American Journal of Epidemiology. 2018; 187(3):576-584. PMID: 29165547. PMCID: PMC5860387
  • Schisterman EF, Perkins NJ, Mumford SL, Ahrens KA, Mitchell EM. Collinearity and causal diagrams: a lesson on the importance of model specification. Epidemiology. 2017; 28(1):47-53. PMID: 27676260. PMCID: PMC5131787.
  • Hinkle SN, Mitchell EM, Grantz KL, Ye A, Schisterman EF. Maternal Weight Gain During Pregnancy: Comparing Methods to Address Bias Due to Length of Gestation in Epidemiological Studies. Paediatric and Perinatal Epidemiology. 2016; 30(3):294-304. PMID: 26916673. PMCID: PMC4818698
  • Mitchell EM, Hinkle SN, Schisterman EF. It's About Time: A Survival Approach to Gestational Weight Gain and Preterm Delivery. Epidemiology. 2016; 27(2):182-187. PMID: 26489043.
  • Schisterman EF, Sjaarda LA. No Right Answers without Knowing Your Question. Paediatric and Perinatal Epidemiology. 2016; 30(1):20-22. PMID: 26768057. PMCID: PMC4721257
  • Ahrens KA, Cole SR, Westreich D, Platt RW, Schisterman EF. A cautionary note about estimating effects of secondary exposures in cohort studies. American Journal of Epidemiology. 2015; 181(3):198-203. PMID: 25589243. PMCID: PMC4312425.
  • Mumford SL, Schisterman EF, Cole SR, Westreich D, Platt RW. Time at risk and intention-to-treat analyses: parallels and implications for inference. Epidemiology. 2015; 26(1):112-118. PMID: 25275571.
  • Schisterman EF, Cole SR, Ye A, Platt RW. Accuracy loss due to selection bias in cohort studies with left truncation. Paediatric and Perinatal Epidemiology. 2013; 27(5):491-502. PMID: 23930785.
  • VanderWeele TJ, Mumford SL, Schisterman EF. Conditioning on intermediates in perinatal epidemiology. Epidemiology. 2012; 23(1):1-9. PMID: 22157298. PMCID: PMC3240847.
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