# Development and Testing of Response Surface Methods for Investigating the Epidemiology of Exposure to Mixtures

> **NIH NIH R01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $411,763

## Abstract

According to NIEHS, “It is imperative to develop methods to assess the health effects associated with complex
exposures in order to minimize their impact on the development of disease.” NIEHS has held several meetings
on mixtures, including the 2015 workshop on Statistical Approaches for Assessing Health Effects of
Environmental Chemical Mixtures in Epidemiology Studies. Conclusions include the following. 1) An
interdisciplinary perspective is needed, including insights from environmental epidemiology,
statistics/mathematics, toxicology and exposure science. 2) Mixtures epidemiology has three key goals: a)
identify components of a mixture contributing to the outcome; b) examine interactions between the
components; c) construct summary measures of exposure where possible. 3) Different methods have different
strengths and weaknesses that may be complementary. We propose to build upon three methods that
performed well at the 2015 workshop: Bayesian kernel machine regression (BKMR), exposure space
smoothing (ESS) and weighted quantile sum regression (WQS). We will develop two complementary methods:
1) BKMR/ESS. We will expand and combine aspects of BKMR and ESS into one method that primarily
addresses the first two goals: variable selection and interactions. Crucial aspects of our proposal are i)
extension to binary health outcomes, the most common type of outcome data in epidemiology (the 2015
NIEHS workshop examined continuous outcomes); ii) variable selection using the hierarchical structures
observed for correlations between exposures; iii) incorporation of toxicological information. 2) Single index
model: We will evaluate a generalization of WQS, the single index model (SIM). SIM non-parametrically
estimates a one-dimensional smooth function of a weighted sum of exposures. The weighted sum represents a
summary measure of exposure (one based on toxicological principles), a third goal of mixtures epidemiology.
Following method development, we will test the methods using both synthetic and real world data sets,
including the Environment And Reproductive Health (EARTH) cohort study. We will incorporate causal
inference tools such as directed acyclic graphs (DAGs). For example, correlated exposures (co-exposures) are
confounders under some DAGs and colliders or intermediate variables under others. This must be taken into
account in both generation of synthetic data and proper interpretation of results. The specific aims of this
project are as follows: Specific Aim 1: Combine features of BKMR and ESS to produce a method for analyzing
epidemiologic data that incorporates toxicological information; can handle continuous, binary and repeated
measures outcome data; select important exposure variables; flexibly model and examine interactions; adjust
for confounders; is robust to influential points; Specific Aim 2: Evaluate the single index model (SIM) as a
method for analyzing epidemiologic mixtures data and generating exposure summary measures; Specific A...

## Key facts

- **NIH application ID:** 9870928
- **Project number:** 5R01ES028800-03
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Thomas F Webster
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $411,763
- **Award type:** 5
- **Project period:** 2018-02-15 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9870928

## Citation

> US National Institutes of Health, RePORTER application 9870928, Development and Testing of Response Surface Methods for Investigating the Epidemiology of Exposure to Mixtures (5R01ES028800-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9870928. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*