Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures

Yuyan Wang, Akhgar Ghassabian, Bo Gu, Yelena Afanasyeva, Yiwei Li, Leonardo Trasande, Mengling Liu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).

Original languageEnglish
Pages (from-to)2619-2632
Number of pages14
JournalBiometrics
Volume79
Issue number3
DOIs
StatePublished - Sep 2023
Externally publishedYes

Keywords

  • functional data
  • quantile regression
  • repeated measures
  • single index
  • splines

Fingerprint

Dive into the research topics of 'Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures'. Together they form a unique fingerprint.

Cite this