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A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes.
Wang, Yuyan; Wu, Yinxiang; Jacobson, Melanie H; Lee, Myeonggyun; Jin, Peng; Trasande, Leonardo; Liu, Mengling.
Afiliação
  • Wang Y; Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY, 10016, USA.
  • Wu Y; Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY, 10016, USA.
  • Jacobson MH; Department of Pediatrics, NYU Langone Health, New York, NY, USA.
  • Lee M; Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY, 10016, USA.
  • Jin P; Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY, 10016, USA.
  • Trasande L; Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY, 10016, USA.
  • Liu M; Department of Pediatrics, NYU Langone Health, New York, NY, USA.
Environ Health ; 19(1): 96, 2020 09 11.
Article em En | MEDLINE | ID: mdl-32912175
ABSTRACT

BACKGROUND:

Statistical 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.

METHODS:

We 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.

RESULTS:

PLSI 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.

CONCLUSIONS:

We 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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Triglicerídeos / Exposição Ambiental / Poluentes Ambientais Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Triglicerídeos / Exposição Ambiental / Poluentes Ambientais Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article