國立虎尾科技大學 |

Statistical methods for environmental mixtures = a primer in environmental epidemiology /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical methods for environmental mixtures/ by Andrea Bellavia.
Reminder of title:	a primer in environmental epidemiology /
Author:	Bellavia, Andrea.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xi, 99 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Environmental health - Statistical methods. -
Online resource:	https://doi.org/10.1007/978-3-031-78987-8
ISBN:	9783031789878

Statistical methods for environmental mixtures = a primer in environmental epidemiology /
Bellavia, Andrea.

Statistical methods for environmental mixturesa primer in environmental epidemiology /[electronic resource] :by Andrea Bellavia. - Cham :Springer Nature Switzerland :2025. - xi, 99 p. :ill. (some col.), digital ;24 cm. - Society, environment and statistics,2948-2771. - Society, environment and statistics..

Preface -- Chapter 1 Environmental Mixtures -- Chapter 2 Characterizing Environmental Mixtures -- Chapter 3 Regression-Based Approaches for Mixture-Health Associations -- Chapter 4 Mixture Indexing Approaches -- Chapter 5 Flexible Approaches for Complex Settings -- Chapter 6 Additional Topics and Final Remarks.

This book provides a comprehensive introduction to statistical approaches for the assessment of complex environmental exposures, such as pollutants and chemical mixtures, within the exposome framework. Environmental mixtures are defined as groups of 3 or more chemical/pollutants, simultaneously present in nature, consumer products, or in the human body. Assessing the health effects of environmental mixtures poses several methodological challenges due to the high levels of correlation that are often present between environmental chemicals, and by the need of incorporating flexible non-additive and non-linear effects that can capture and describe the complex mechanisms by which environmental exposure contribute to diseases. Several statistical approaches are proposed and discussed, including the application of regression-based approaches (e.g. penalized regression such as LASSO and elastic net, or Bayesian variable selection) for environmental exposures, and novel methods (e.g. weighted quantile sum regression, or Bayesian Kernel Machine Regression) that account for specific complexities of environmental exposures. More recent efforts included are the application of machine learning approaches (e.g. gradient boosting) for environmental data. Statistical Methods for Environmental Mixtures describes the statistical challenges that commonly arise when dealing with environmental exposures and provides an introduction to different statistical approaches for such data. Over the last decade, substantial efforts have been made to transition the statistical framework for environmental exposures in epidemiologic studies from a single-chemical/pollutant to a multi-chemicals/pollutants approach. This book provides a comprehensive introduction to this modern multi-chemicals/pollutants framework. Emphasis is given to interpretability, discussing issues with causal interpretation and translation of scientific finding when applying the discussed statistical approaches for complex environmental exposures. The target audience includes researchers in environmental epidemiology and applied statisticians working in the field. As such, while rigorously presenting the statistical methodologies, the book keeps an applied focus, discussing those settings where each method is appropriate for use and for which question it can be applied, providing examples of accurate presentation and interpretation from the literature, including a basic introduction to R packages and tutorials, as well as discussing assumptions and practical challenges when applying these techniques on real data.

ISBN: 9783031789878

Standard No.: 10.1007/978-3-031-78987-8doiSubjects--Topical Terms:

1488428
Environmental health
--Statistical methods.

LC Class. No.: RA566.26

Dewey Class. No.: 616.9800151

Statistical methods for environmental mixtures = a primer in environmental epidemiology /
LDR:03998nam a2200337 a 4500 001 1161499
003 DE-He213
005 20250128115231.0
006 m d
007 cr nn 008maaau
008 251029s2025 sz s 0 eng d
020 $a 9783031789878 $q (electronic bk.)
020 $a 9783031789861 $q (paper)
024 7 $a 10.1007/978-3-031-78987-8 $2 doi
035 $a 978-3-031-78987-8
040 $a GP $c GP
041 0 $a eng
050 4 $a RA566.26
072 7 $a PBT $2 bicssc
072 7 $a MED090000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 616.9800151 $2 23
090 $a RA566.26 $b .B437 2025
100 1 $a Bellavia, Andrea. $3 1488427
245 1 0 $a Statistical methods for environmental mixtures $h [electronic resource] : $b a primer in environmental epidemiology / $c by Andrea Bellavia.
260 $a Cham : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xi, 99 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Society, environment and statistics, $x 2948-2771
505 0 $a Preface -- Chapter 1 Environmental Mixtures -- Chapter 2 Characterizing Environmental Mixtures -- Chapter 3 Regression-Based Approaches for Mixture-Health Associations -- Chapter 4 Mixture Indexing Approaches -- Chapter 5 Flexible Approaches for Complex Settings -- Chapter 6 Additional Topics and Final Remarks.
520 $a This book provides a comprehensive introduction to statistical approaches for the assessment of complex environmental exposures, such as pollutants and chemical mixtures, within the exposome framework. Environmental mixtures are defined as groups of 3 or more chemical/pollutants, simultaneously present in nature, consumer products, or in the human body. Assessing the health effects of environmental mixtures poses several methodological challenges due to the high levels of correlation that are often present between environmental chemicals, and by the need of incorporating flexible non-additive and non-linear effects that can capture and describe the complex mechanisms by which environmental exposure contribute to diseases. Several statistical approaches are proposed and discussed, including the application of regression-based approaches (e.g. penalized regression such as LASSO and elastic net, or Bayesian variable selection) for environmental exposures, and novel methods (e.g. weighted quantile sum regression, or Bayesian Kernel Machine Regression) that account for specific complexities of environmental exposures. More recent efforts included are the application of machine learning approaches (e.g. gradient boosting) for environmental data. Statistical Methods for Environmental Mixtures describes the statistical challenges that commonly arise when dealing with environmental exposures and provides an introduction to different statistical approaches for such data. Over the last decade, substantial efforts have been made to transition the statistical framework for environmental exposures in epidemiologic studies from a single-chemical/pollutant to a multi-chemicals/pollutants approach. This book provides a comprehensive introduction to this modern multi-chemicals/pollutants framework. Emphasis is given to interpretability, discussing issues with causal interpretation and translation of scientific finding when applying the discussed statistical approaches for complex environmental exposures. The target audience includes researchers in environmental epidemiology and applied statisticians working in the field. As such, while rigorously presenting the statistical methodologies, the book keeps an applied focus, discussing those settings where each method is appropriate for use and for which question it can be applied, providing examples of accurate presentation and interpretation from the literature, including a basic introduction to R packages and tutorials, as well as discussing assumptions and practical challenges when applying these techniques on real data.
650 0 $a Environmental health $x Statistical methods. $3 1488428
650 0 $a Epidemiology $x Statistical methods. $3 592904
650 0 $a Biometry. $3 598268
650 0 $a Regression analysis. $3 569541
650 1 4 $a Biostatistics. $3 783654
650 2 4 $a Bayesian Inference. $3 1211345
650 2 4 $a Linear Models and Regression. $3 1366135
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Society, environment and statistics. $3 1434734
856 4 0 $u https://doi.org/10.1007/978-3-031-78987-8
950 $a Mathematics and Statistics (SpringerNature-11649)