Biomarkers are, and will continue to be, an integral part of epidemiological research, making substantial contributions to our understanding of disease pathways and processes. New and emerging biomarkers are essential to this continued understanding. As such, novel study designs that reduce cost and leverage statistical efficiency are also a major focus of Division researchers (Lash and Schisterman, Epidemiology 2017; Schildcrout et al. Epidemiology 2017; Schildcrout et al. Epidemiology 2017). These methods focus on outcome dependent sampling were cost is reduced by measuring biomarkers in a principally designed subset of the samples in the full cohort. Efficiency is maximized by designing a subset guided by the outcome of interest which is often known prior to expensive biomarker exposure measurement. Division researchers continue to adapt these efficient designs and develop methods making outcome dependent sampling equally useful in the analysis of a broad spectrum of cross sectional as well as longitudinal epidemiologic data. These issues have served as the motivation for numerous papers, as well as a collaborative effort funded by the Long-Range Research Initiative of the American Chemistry Council, with the goal of providing the methodological tools necessary to assess and address issues related to study design, biomarker measurement, and biomarker analytic assessment.
| Vernet, Céline; Philippat, Claire; Calafat, Antonia M et al. (2018) Within-Day, Between-Day, and Between-Week Variability of Urinary Concentrations of Phenol Biomarkers in Pregnant Women. Environ Health Perspect 126:037005 |
| Van Domelen, Dane R; Mitchell, Emily M; Perkins, Neil J et al. (2018) Logistic regression with a continuous exposure measured in pools and subject to errors. Stat Med 37:4007-4021 |
| Pollack, Anna Z; Mumford, Sunni L; Krall, Jenna R et al. (2018) Exposure to bisphenol A, chlorophenols, benzophenones, and parabens in relation to reproductive hormones in healthy women: A chemical mixture approach. Environ Int 120:137-144 |
| Schildcrout, Jonathan S; Schisterman, Enrique F; Aldrich, Melinda C et al. (2018) Outcome-related, Auxiliary Variable Sampling Designs for Longitudinal Binary Data. Epidemiology 29:58-66 |
| Sjaarda, Lindsey A; Ahrens, Katherine A; Kuhr, Daniel L et al. (2018) Pilot study of placental tissue collection, processing, and measurement procedures for large scale assessment of placental inflammation. PLoS One 13:e0197039 |
| Schildcrout, Jonathan S; Schisterman, Enrique F; Mercaldo, Nathaniel D et al. (2018) Extending the Case-Control Design to Longitudinal Data: Stratified Sampling Based on Repeated Binary Outcomes. Epidemiology 29:67-75 |
| Ananth, Cande V; Schisterman, Enrique F (2018) Reply. Am J Obstet Gynecol 218:366-367 |
| Harel, Ofer; Mitchell, Emily M; Perkins, Neil J et al. (2018) Multiple Imputation for Incomplete Data in Epidemiologic Studies. Am J Epidemiol 187:576-584 |
| Lash, Timothy L; Schisterman, Enrique F (2018) New Designs for New Epidemiology. Epidemiology 29:76-77 |
| Sun, BaoLuo; Perkins, Neil J; Cole, Stephen R et al. (2018) Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data. Am J Epidemiol 187:585-591 |
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