ArticleEnvironmental health : a global access science source2020
A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes.
Article in Environmental health : a global access science source, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 17 citations in OpenAlex.
- Air pollution and autism-like traits: Sensitive periods and joint effects of exposure to sources and constituents of fine particulate matter.Environmental research · 2026Article
- Comparison of environmental mixture methods for estimating the joint effects of environmental mixtures.Environmental research · 2026Article
- Flexible and Interpretable Modeling of Overlapping Exposure Risks in Self-Controlled Case Series Analysis.Statistics in medicine · 2026Article
- Multiple-index interaction models to accommodate exposure grouping in environmental mixtures.Biometrics · 2026Article
- Tailoring Bayesian Additive Regression Trees (BART) for environmental mixture studies.PloS one · 2026Article
- Mapping war trauma: A machine learning approach to predict mental health impacts in Ukraine.SSM - population health · 2025Article
- Dynamic Single-Index Scalar-On-Function Model.Statistics in medicine · 2025Article
- Prenatal phthalate exposure and anogenital distance in infants at 12 months.Environment international · 2025Article
- Prenatal exposure to environmental bisphenols over time and their association with childhood asthma, allergic rhinitis and atopic dermatitis in the ECHO consortium.Environmental pollution (Barking, Essex : 1987) · 2025Article
- Partial-linear single-index Cox regression models with multiple time-dependent covariates.BMC medical research methodology · 2024Article
- Evaluating associations of bisphenol and phthalate exposure with time to pregnancy and subfecundity in a New York City pregnancy cohort.Environmental pollution (Barking, Essex : 1987) · 2024Article
- Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures.Biometrics · 2023Article
- Bayesian multiple index models for environmental mixtures.Biometrics · 2023Article
- Variability and correlations of synthetic chemicals in urine from a New York City-based cohort of pregnant women.Environmental pollution (Barking, Essex : 1987) · 2022Article
- Determinants of phthalate exposures in pregnant women in New York City.Environmental research · 2022Article
- Partially linear single-index generalized mean residual life models.Statistics in medicine · 2021Article
- Organophosphate pesticide exposure: Demographic and dietary predictors in an urban pregnancy cohort.Environmental pollution (Barking, Essex : 1987) · 2021Article
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7 authors at 1 institution in 1 country.
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Abstract
backgroundStatistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility.
methodsWe presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models.
resultsPLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects.
conclusionsWe presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.
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