Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes

Naveen N. Narisetty, Bhramar Mukherjee, Yin Hsiu Chen, Richard Gonzalez, John D. Meeker

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures. However, this issue becomes analytically challenging in the presence of potential nonlinearity in the outcome-exposure response surface and a set of correlated exposures. Through simulation studies and analyses of test datasets that were simulated as a part of a data challenge in multipollutant modeling organized by the National Institute of Environmental Health Sciences (http://www.niehs.nih.gov/about/events/pastmtg/2015/statistical/), we illustrate the advantages of our proposed method in comparison with existing alternative approaches. A particular strength of our method is that it demonstrates very low false positives across empirical studies. Our method is also used to analyze a dataset that was released from the Health Outcomes and Measurement of the Environment Study as a benchmark beta-tester dataset as a part of the same workshop.

Original languageEnglish (US)
Pages (from-to)1582-1600
Number of pages19
JournalStatistics in Medicine
Volume38
Issue number9
DOIs
StatePublished - Apr 30 2019

Keywords

  • environmental exposures
  • interaction selection
  • multipollutant research
  • nonlinear effects

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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