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Activity Number: 462 - Novel Spatial and Spatio-Temporal Models in Public Health
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312851
Title: Treed Distributed Lag Nonlinear Models
Author(s): Daniel Mork* and Ander Wilson
Companies: Colorado State University and Colorado State University
Keywords: regression trees; distributed lag; air pollution; children’s health; critical windows

The distributed lag non-linear models (DLNM) are used to estimate an exposure-time-response function when it is postulated the exposure effect is non-linear. Standard DLNM methods estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as penalized splines assume smoothness, which may be unrealistic when the exposure is associated with the outcome only in a specific time window. We propose a non-parametric framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space with boundary points estimated by dichotomous splits of the exposure and time dimensions. Each regression tree contributes a partial estimate and their sum allows for smoothness in the DLNM when necessary. In a simulation, we show advantages to our approach. We apply our non-parametric DLNM methods to estimate the association between maternal exposure to PM2.5 and birth weight in Colorado front-range births.

Authors who are presenting talks have a * after their name.

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