LEADER 04950nam 22006855 450 001 9910983087203321 005 20250919143029.0 010 $a9783031789878 010 $a3031789873 024 7 $a10.1007/978-3-031-78987-8 035 $a(CKB)37391007400041 035 $a(MiAaPQ)EBC31889083 035 $a(Au-PeEL)EBL31889083 035 $a(DE-He213)978-3-031-78987-8 035 $a(OCoLC)1499720415 035 $a(EXLCZ)9937391007400041 100 $a20250128d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Methods for Environmental Mixtures $eA Primer in Environmental Epidemiology /$fby Andrea Bellavia 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (148 pages) 225 1 $aSociety, Environment and Statistics,$x2948-2771 311 08$a9783031789861 311 08$a3031789865 327 $aPreface -- 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. 330 $aThis 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. 410 0$aSociety, Environment and Statistics,$x2948-2771 606 $aBiometry 606 $aStatistics 606 $aRegression analysis 606 $aBiostatistics 606 $aBayesian Inference 606 $aLinear Models and Regression 606 $aBiometria$2thub 606 $aEstadística$2thub 606 $aAnàlisi de regressió$2thub 608 $aLlibres electrònics$2thub 615 0$aBiometry. 615 0$aStatistics. 615 0$aRegression analysis. 615 14$aBiostatistics. 615 24$aBayesian Inference. 615 24$aLinear Models and Regression. 615 7$aBiometria 615 7$aEstadística 615 7$aAnàlisi de regressió 676 $a570.15195 700 $aBellavia$b Andrea$01785694 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983087203321 996 $aStatistical Methods for Environmental Mixtures$94317187 997 $aUNINA