# SEMIPARAMETRIC METHODS FOR MODELING OF TIME-DEPENDENT ENVIRONMENTAL EXPOSURES

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $343,238

## Abstract

SEMIPARAMETRIC METHODS FOR MODELING OF TIME-DEPENDENT ENVIRONMENTAL EXPOSURES
Abstract
 Countless environmental exposures throughout the human life course and influence human development
and susceptibility to disease. Assessing how multiple exposures synergize or antagonize each other to affect
human health is a powerful approach to understanding the all-around impact of environmental exposures.
Such approach will be needed in the new paradigm of exposome research, which encompass all non-genetic
causes for diseases, from external natural to social environments and from internal macro- to
microenvironments. However, many environmental investigations are challenged by the special data structure
and characteristics of time-dependent exposures. In particular, multiple environmental exposures: 1) are inter-
correlated; 2) are time-dependent; 3) exhibit time-varying effects; 4) have heterogeneous effects; and 5)
demonstrate complex and nonlinear exposure-response relationships. The limiting availability of statistical
models and analytical tools to handle these challenges hinder our ability to make inference or draw
conclusions about the effects of multiple exposures on human health. Building upon our experience developing
statistical methodologies and motivated by the challenges encountered in many of our collaborations in
environmental research, we propose to develop and implement novel statistical methods to address important
scientific questions in environmental health research, specifically through the following aims: 1) evaluate the
effects of time-dependent environmental exposures on time-invariant health outcomes and identify critical
windows of vulnerability; 2) characterize the effects of time-dependent environmental exposures on time-to-
event and longitudinal outcomes; 3) investigate the heterogeneous impacts by environmental exposures on
subpopulations; and 4) develop, distribute, and support open-source software packages for the proposed
methods. All proposed models, estimation, and testing procedures will be investigated through analytical
approaches, theoretical inference, numerical simulations, and applications to multiple datasets from approved
human-subject studies. Upon its completion, the proposed project will provide new statistical methods that both
support ongoing collaborations to address critical scientific questions and enable the environmental health
research community to better assess the impact of time-dependent exposures.

## Key facts

- **NIH application ID:** 10180693
- **Project number:** 1R01ES032808-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Mengling Liu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $343,238
- **Award type:** 1
- **Project period:** 2021-04-09 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10180693

## Citation

> US National Institutes of Health, RePORTER application 10180693, SEMIPARAMETRIC METHODS FOR MODELING OF TIME-DEPENDENT ENVIRONMENTAL EXPOSURES (1R01ES032808-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10180693. Licensed CC0.

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