At-risk-measure Sampling in Case-Control Studies with Aggregated Data

• Type of Material: Article
• Authors: Michael D. Garber, Emory University; Lauren McCullough, Emory University; Stephen J. Mooney, University of Washington; Michael Kramer, Emory University; Kari E. Watkins, Georgia Institute of Technology; Felipe Lobelo, Emory University; William Flanders, Emory University
• Language: English
• Date: 2021-01-01
• Publisher: LIPPINCOTT WILLIAMS & WILKINS
• Publication Version: Final Published Version
• Copyright Statement: © 2020 The Author(s).
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1097/EDE.0000000000001268
• Title of Journal or Parent Work: EPIDEMIOLOGY
• Volume: 32
• Issue: 1
• Start Page: 101
• End Page: 110
• Grant/Funding Information: M.D.G. was supported by the National Heart, Lung, and Blood Institute (F31HL143900) and by the Doctoral Student Research Grant from the American College of Sports Medicine Foundation (18-00663). S.J.M. was supported by the National Library of Medicine (R00LM012868). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the American College of Sports Medicine Foundation.
• Keywords: EXPOSURE; Life Sciences & Biomedicine; GPS; INFORMATION; BICYCLISTS; Public, Environmental & Occupational Health; control studies; DESIGN; Location-based studies; ROAD CHARACTERISTICS; Epidemiological monitoring; Big data; Case– INJURY; POPULATION; Sampling studies; Epidemiologic studies; TIME; CASE-CROSSOVER; Science & Technology
• Research Categories: Health Sciences, Epidemiology; Health Sciences, Public Health

Abstract

Transient exposures are difficult to measure in epidemiologic studies, especially when both the status of being at risk for an outcome and the exposure change over time and space, as when measuring built-environment risk on transportation injury. Contemporary "big data"generated by mobile sensors can improve measurement of transient exposures. Exposure information generated by these devices typically only samples the experience of the target cohort, so a case-control framework may be useful. However, for anonymity, the data may not be available by individual, precluding a case-crossover approach. We present a method called at-risk-measure sampling. Its goal is to estimate the denominator of an incidence rate ratio (exposed to unexposed measure of the at-risk experience) given an aggregated summary of the at-risk measure from a cohort. Rather than sampling individuals or locations, the method samples the measure of the at-risk experience. Specifically, the method as presented samples person-distance and person-events summarized by location. It is illustrated with data from a mobile app used to record bicycling. The method extends an established case-control sampling principle: sample the at-risk experience of a cohort study such that the sampled exposure distribution approximates that of the cohort. It is distinct from density sampling in that the sample remains in the form of the at-risk measure, which may be continuous, such as person-time or person-distance. This aspect may be both logistically and statistically efficient if such a sample is already available, for example from big-data sources like aggregated mobile-sensor data.