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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.

Yuyan WangYinxiang WuMelanie H JacobsonMyeonggyun LeePeng JinLeonardo TrasandeMengling Liu
Published in: Environmental health : a global access science source (2020)
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.
Keyphrases
  • air pollution
  • systematic review
  • deep learning
  • type diabetes
  • adipose tissue
  • metabolic syndrome
  • cross sectional
  • mass spectrometry
  • skeletal muscle
  • neural network
  • glycemic control