nhanesA: achieving transparency and reproducibility in NHANES research

Author:

Ale Laha1ORCID,Gentleman Robert2,Sonmez Teresa Filshtein3,Sarkar Deepayan4,Endres Christopher5

Affiliation:

1. School of Computing and Artificial Intelligence, Southwest Jiaotong University , No. 999, Xian Rd, Pidu Dist., Chengdu, Sichuan 611756, China

2. Center for Computational Biomedicine, Harvard Medical School , 25 Shattuck St, Boston, MA 02115, USA

3. Research Department, 23 and Me, Inc. , 223 N Mathilda Ave, Sunnyvale, CA 94086, USA

4. Theoretical Statistics and Mathematics Unit, Indian Statistical Institute , 7 SJSS Marg, New Delhi 110016, India

5. The Promenade Dance Studio, Inc. , 2605 Lord Baltimore Drive, Windsor Mill, MD 21244, USA

Abstract

Abstract The National Health and Nutrition Examination Survey provides comprehensive data on demographics, sociology, health and nutrition. Conducted in 2-year cycles since 1999, most of its data are publicly accessible, making it pivotal for research areas like studying social determinants of health or tracking trends in health metrics such as obesity or diabetes. Assembling the data and analyzing it presents a number of technical and analytic challenges. This paper introduces the nhanesA R package, which is designed to assist researchers in data retrieval and analysis and to enable the sharing and extension of prior research efforts. We believe that fostering community-driven activity in data reproducibility and sharing of analytic methods will greatly benefit the scientific community and propel scientific advancements. Database URL: https://github.com/cjendres1/nhanes

Publisher

Oxford University Press (OUP)

Reference44 articles.

1. National Health and Nutrition Examination Survey;CDC

2. Library of Medicine at the National Institutes of Health;NIH

3. Developing an exposure burden score for chemical mixtures using item response theory, with applications to PFAS mixtures;Liu;Environ. Health Perspect.,2022

4. Activity level as a mortality predictor in a population sample after typical underwriting exclusions and laboratory scoring;Rigatti;J. Insur. Med.,2020

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