Statistical Methods for Environmental Epidemiology with R

Advances in statistical methodology and computing have played an important role in allowing researchers to more accurately assess the health effects of ambient air pollution. The methods and software developed in this area are applicable to a wide array of problems in environmental epidemiology. Statistical Methods for Environmental Epidemiology with R provides an overview of the methods used for investigating the health effects of air pollution and gives examples and case studies in R which demonstrate the application of those methods to real data. Statistical Methods for Environmental Epidemiology with R will be useful to statisticians, epidemiologists, and graduate students working in the area of air pollution and health and others analyzing similar data.

The authors describe the different existing approaches to statistical modeling and cover basic aspects of analyzing and understanding air pollution and health data. The case studies in each chapter demonstrate how to use R to apply and interpret different statistical models and to explore the effects of potential confounding factors. A working knowledge of R and regression modeling is assumed. In-depth knowledge of R programming is not required to understand and run the examples.

Researchers in this area will find Statistical Methods for Environmental Epidemiology with R useful as a ``live'' reference. Software for all of the analyses in the book is downloadable from the web and is available under a Free Software license. The reader is free to run the examples in Statistical Methods for Environmental Epidemiology with R and modify the code to suit their needs. In addition to providing the software for developing the statistical models, the authors provide the entire database from the National Morbidity Mortality and Air Pollution Study (NMMAPS) in a convenient R package. With the database, readers can run the examples and experiment with their own methods and ideas.

- Studies of air pollution and health
- Introduction to R and air pollution and health data
- Reproducible research tools
- Statistical issues in estimating the health effects of spatial-temporal environmental exposures
- Exploratory data analyses
- Statistical models
- Pooling risks across locations and quantifying spatial heterogeneity
- A reproducible seasonal analysis of PM10 and mortaility in the U.S.