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Sampling and Sampling Frames in Big Data Epidemiology

  • Genetic Epidemiology (C Amos, Section Editor)
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Abstract

Purpose of Review

The ‘big data’ revolution affords the opportunity to reuse administrative datasets for public health research. While such datasets offer dramatically increased statistical power compared with conventional primary data collection, typically at much lower cost, their use also raises substantial inferential challenges. In particular, it can be difficult to make population inferences because the sampling frames for many administrative datasets are undefined. We reviewed options for accounting for sampling in big data epidemiology.

Recent Findings

We identified three common strategies for accounting for sampling when the data available were not collected from a deliberately constructed sample: (1) explicitly reconstruct the sampling frame, (2) test the potential impacts of sampling using sensitivity analyses, and (3) limit inference to sample.

Summary

Inference from big data can be challenging because the impacts of sampling are unclear. Attention to sampling frames can minimize risks of bias.

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Funding

This work was supported by a grant from the National Library of Medicine (1K99LM012868) and the National Heart, Lung, And Blood Institute (F31HL143900). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Department of Epidemiology, University of Washington, 1959 NE Pacific Street, Health Sciences Bldg, F-262, Box 357236, Seattle, WA, 98195, USA

    Stephen J. Mooney

  2. Harborview Injury Prevention and Research Center, University of Washington, Seattle, WA, USA

    Stephen J. Mooney

  3. Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA

    Michael D. Garber

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  1. Stephen J. Mooney
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  2. Michael D. Garber
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Corresponding author

Correspondence to Stephen J. Mooney.

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Conflict of Interest

Stephen J. Mooney reports grants from National Library of Medicine, and the Better Bike Share Coalition during the conduct of the study. Michael D. Garber reports grants from National Heart, Lung, and Blood Institute and from American College of Sports Medicine during the conduct of the study.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Mooney, S.J., Garber, M.D. Sampling and Sampling Frames in Big Data Epidemiology. Curr Epidemiol Rep 6, 14–22 (2019). https://doi.org/10.1007/s40471-019-0179-y

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