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Towards Systematic Methods in an Era of Big Data: Neighborhood Wide Association Studies

  • Shannon M. LynchEmail author
Chapter
Part of the Energy Balance and Cancer book series (EBAC, volume 15)

Abstract

Methodologic challenges related to variable selection exist in neighborhood studies. In the era of “Big Data”, this variable selection issue will only continue to grow as neighborhood data become increasingly more complex and integrated with multilevel data. To allow for consistency and comparability of neighborhood variables across studies, systematic approaches for variable selection are needed. Borrowing concepts from empiric methods in biology, a novel neighborhood-wide association study (NWAS) and a neighborhood-environment wide association study (NE-WAS) were recently developed. This chapter introduces key concepts of the NWAS/NE-WAS designs, provides criteria for evaluating these systematic approaches, and discusses the potential impact these empiric methods have on future multilevel interventions.

Keywords

Neighborhood wide association study (NWAS) Neighborhood-environment wide association study (NE-WAS) Big data Machine learning Variable selection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fox Chase Cancer CenterPhiladelphiaUSA

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