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Advanced Research and Data Methods in Women's Health: Big Data Analytics, Adaptive Studies, and the Road Ahead

Macedonia, Christian R. MD; Johnson, Clark T. MD, MPH; Rajapakse, Indika PhD

doi: 10.1097/AOG.0000000000001865
Contents: Clinical Expert Series
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Technical advances in science have had broad implications in reproductive and women's health care. Recent innovations in population-level data collection and storage have made available an unprecedented amount of data for analysis while computational technology has evolved to permit processing of data previously thought too dense to study. “Big data” is a term used to describe data that are a combination of dramatically greater volume, complexity, and scale. The number of variables in typical big data research can readily be in the thousands, challenging the limits of traditional research methodologies. Regardless of what it is called, advanced data methods, predictive analytics, or big data, this unprecedented revolution in scientific exploration has the potential to dramatically assist research in obstetrics and gynecology broadly across subject matter. Before implementation of big data research methodologies, however, potential researchers and reviewers should be aware of strengths, strategies, study design methods, and potential pitfalls. Examination of big data research examples contained in this article provides insight into the potential and the limitations of this data science revolution and practical pathways for its useful implementation.

Advances in genomics, health informatics, imaging, and adaptive study design, all accelerated by innovations in advanced data methods, will enlighten our understanding of reproductive health and human development.

Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, Maryland; and the Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan.

Corresponding author: Christian R. Macedonia, MD, Suite 200, 694 Good Drive, Lancaster, PA 17601; email: Cmacedo2@jhmi.edu.

Financial Disclosure The authors did not report any potential conflicts of interest.

For a “Glossary of Advanced Data Methods in Biomedical Research” related to this article, see Appendix 1, online at http://links.lww.com/AOG/A915.

Continuing medical education for this article is available at http://links.lww.com/AOG/A916.

Each author has indicated that he or she has met the journal's requirements for authorship.

© 2017 by The American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.