Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low‐rank kriging multiple membership model

Joseph Boyle; Mary H. Ward; James R. Cerhan; Nat Rothman; David C. Wheeler

doi:10.1002/sim.9587

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Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low‐rank kriging multiple membership model

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Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low‐rank kriging multiple membership model

Joseph Boyle, Mary H. Ward, James R. Cerhan, Nat Rothman and David C. Wheeler

Statistics in medicine, Vol.41(29), pp.5679-5697

12/20/2022

DOI: 10.1002/sim.9587

PMCID: PMC9691549

PMID: 36161724

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Abstract

The exposome is an ideal in public health research that posits that individuals experience risk for adverse health outcomes from a wide variety of sources over their lifecourse. There have been increases in data collection in the various components of the exposome, but novel statistical methods are needed that capture multiple dimensions of risk at once. We introduce a Bayesian index low‐rank kriging (LRK) multiple membership model (MMM) to simultaneously estimate the health effects of one or more groups of exposures, the relative importance of exposure components, and cumulative spatial risk over time using residential histories. The model employs an MMM to consider all residential locations for subjects weighted by duration and LRK to increase computational efficiency. We demonstrate the performance of the Bayesian index LRK‐MMM through a simulation study, showing that the model accurately and consistently estimates the health effects of one or several group indices and has high power to identify a region of elevated spatial risk due to unmeasured environmental exposures. Finally, we apply our model to data from a multicenter case‐control study of non‐Hodgkin lymphoma (NHL), finding a significant positive association between one index of pesticides and risk for NHL in Iowa. Additionally, we find an area of significantly elevated spatial risk for NHL in Los Angeles. In conclusion, our Bayesian index LRK‐MMM represents a step forward toward bringing the ideals of the exposome into practice for environmental risk analyzes.

exposome

mixture analysis

non‐Hodgkin lymphoma

residential history

spatial analysis

Details

Title: Subtitle: Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low‐rank kriging multiple membership model
Creators: Joseph Boyle - Virginia Commonwealth University
Mary H. Ward - National Cancer Institute
James R. Cerhan - Mayo Clinic
Nat Rothman - National Cancer Institute
David C. Wheeler - Virginia Commonwealth University
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.41(29), pp.5679-5697
DOI: 10.1002/sim.9587
PMID: 36161724
PMCID: PMC9691549
NLM abbreviation: Stat Med
ISSN: 0277-6715
eISSN: 1097-0258
Publisher: John Wiley & Sons, Inc
Number of pages: 19
Grant note: National Institutes of Health National Cancer Institute (U01CA25937)
Language: English
Date published: 12/20/2022
Academic Unit: Epidemiology
Record Identifier: 9984368082202771

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