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EB Research - Methodological Research in Epidemiology
Causal Inference in Reproductive and Perinatal Epidemiology
Here we extend 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, our objective is 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 areas of:
- The Birth Weight Paradox: Utilizing 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 here 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.
Enrique F. Schisterman, Ph.D.
- VanderWeele TJ, Mumford SL, Schisterman EF. (2012). Conditioning on intermediates in perinatal epidemiology. Epidemiology, 23(1):1-9. With discussion. PMID: 22157298
- Westreich D, Cole SR, Schisterman EF, Platt RW. (2012). A simulation study of finite-sample properties of marginal structural Cox proportional hazards models. Stat Med., Apr 11. doi: 10.1002/sim.5317. PMID: 22492660
- Cole SR, Platt RW, Schisterman EF, Chu H, Westreich D, Richardson D, & Poole C. (2010). Illustrating bias due to conditioning on a collider. International Journal of Epidemiology, 39, 417-420. PMID: 19926667
- Platt RW, Schisterman EF, & Cole SR. (2009). Time modified confounding. American Journal of Epidemiology, 170, 687-694. PMID: 19675141
- Schisterman EF, Whitcomb BW, Mumford S, & Platt RW. (2009). Z-scores and the birth weight paradox. Paediatric and Perinatal Epidemiology, 23, 403-413. PMID: 19689489
- Whitcomb BW, Schisterman EF, Perkins NJ, & Platt R. (2009). Quantification of collider-stratification bias and the birth weight paradox. Paediatric and Perinatal Epidemiology, 23, 394-402. PMID: 19689488
- Schisterman EF, Cole S, & Platt R. (2009). Over-adjustment bias and unnecessary adjustment in epidemiological studies. Epidemiology, 20, 488-495. (with discussion). PMID: 19525685
- Hernández-Díaz S, Wilcox AJ, Schisterman EF, & Hernán MA. (2008). From causal diagrams to birth weight-specific curves of infant mortality. European Journal of Epidemiology, 23, 163-166. PMID: 18224448
- Howards P, Schisterman EF & Heagerty P. (2007). Potential confounding by exposure history and prior outcomes. Epidemiology, 18, 544-551. PMID: 17879426
- Hernández-Díaz S, Schisterman EF, & Hernán MA. (2006). The birth weight "paradox" uncovered. American Journal of Epidemiology, 164, 1115-1120. PMID: 16931543
- Hernández-Díaz S, Schisterman EF, & Hernán MA. (2006). Response to invited commentary: the perils of birth weight - a lesson from directed acyclic graphs. American Journal of Epidemiology, 164, 1124-1125.
- Schisterman EF & Hernández-Díaz S. (2006). Invited commentary: Simple models for a complicated reality. American Journal of Epidemiology, 164, 312-314. PMID: 16847041
- Schisterman EF, Whitcomb BW, Louis GMB, & Louis TA. (2005). Lipid adjustment in the analysis of environmental contaminants and human health risks. Environmental Health Perspectives, 113, 853-857. PMID: 16002372