Preface

This book presents an extended version of the material for the winter class in “Statistical methods for Environmental Mixtures” that I taught at the Department of Environmental Health, Harvard T.H. Chan School of Public Health (HSPH), between 2018 and 2020. The course was designed as a 2-weeks intensive introductory class, which made it realistically impossible to cover all topics and methodologies related to the rapidly expanding field of statistical approaches for high-dimensional exposures and their application in exposome research. As such, the goal of this document is not to comprehensibly summarize the existing literature, but rather to introduce the rational and importance of evaluating environmental exposures as mixtures, to present selected topics within the field, and to provide illustrative examples and critical discussion. The content of the course was designed for students and researchers in environmental health sciences with limited statistical background, and this teaching format has been maintained in this book.

Credits should also go to Dr. Paige Williams and Prof. Brent Coull (from the Department of Biostatistic at HSPH) who gave guest lectures on principal components analysis and Bayesian Kernel Machine Regression during the course: the sections on these topics are largely taken from the material they shared and presented. A special thanks also to Dr. Stefano Renzetti (University of Brescia) who shared relevant material on weighted quantile sum regression.

The R statistical software was used for the practical sessions in class. Despite some introduction to the specific packages and code examples are here provided, the reader should refer to online documentations, provided at various links throughout the document, for detailed descriptions of the software.

About the Author

Andrea Bellavia, PhD

Webpage: andreabellavia.github.io. Twitter: andreabellavia

The author takes full responsibility for the content of this book. Permission was obtained for all figures and tables from published papers that are included.

Copyright 2021 Andrea Bellavia
All rights reserved.

Cover art by Maria Bellavia