Statistical Methods For Environmental Epidemiology With R
Download Statistical Methods For Environmental Epidemiology With R PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Statistical Methods For Environmental Epidemiology With R book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Statistical Methods for Environmental Epidemiology with R
Author: Roger D. Peng
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-12-15
As an area of statistical application, environmental epidemiology and more speci cally, the estimation of health risk associated with the exposure to - vironmental agents, has led to the development of several statistical methods and software that can then be applied to other scienti c areas. The stat- tical analyses aimed at addressing questions in environmental epidemiology have the following characteristics. Often the signal-to-noise ratio in the data is low and the targets of inference are inherently small risks. These constraints typically lead to the development and use of more sophisticated (and pot- tially less transparent) statistical models and the integration of large hi- dimensional databases. New technologies and the widespread availability of powerful computing are also adding to the complexities of scienti c inves- gation by allowing researchers to t large numbers of models and search over many sets of variables. As the number of variables measured increases, so do the degrees of freedom for in uencing the association between a risk factor and an outcome of interest. We have written this book, in part, to describe our experiences developing and applying statistical methods for the estimation for air pollution health e ects. Our experience has convinced us that the application of modern s- tistical methodology in a reproducible manner can bring to bear subst- tial bene ts to policy-makers and scientists in this area. We believe that the methods described in this book are applicable to other areas of environmental epidemiology, particularly those areas involving spatial{temporal exposures.
Statistical Methods in Environmental Epidemiology
Author: Duncan C. Thomas
language: en
Publisher: Oxford University Press, USA
Release Date: 2009
A systematic treatment of the statistical challenges that arise in environmental health studies and the use epidemiologic data in formulating public policy, at a level suitable for graduate students and epidemiologic researchers.
Statistical Methods for Environmental Mixtures
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.