Advanced research and data methods in women's health: Big data analytics, adaptive studies, and the road ahead

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)249-264
Number of pages16
JournalObstetrics and Gynecology
Volume129
Issue number2
DOIs
StatePublished - 2017

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Women's Health
Research Design
Research
Information Storage and Retrieval
Reproductive Health
Gynecology
Obstetrics
Research Personnel
Technology
Delivery of Health Care
Population

ASJC Scopus subject areas

  • Obstetrics and Gynecology

Cite this

Advanced research and data methods in women's health : Big data analytics, adaptive studies, and the road ahead. / Macedonia, Christian; Johnson, Clark; Rajapakse, Indika.

In: Obstetrics and Gynecology, Vol. 129, No. 2, 2017, p. 249-264.

Research output: Contribution to journalArticle

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