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Statistical Methods for Environmental Epidemiology with R : A Case Study in Air Pollution and Health / by Francesca Dominici, Roger D. Peng
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Use R | SpringerLink BücherVerlag: New York, NY : Springer-Verlag New York, 2008Beschreibung: Online-Ressource (X, 144p, digital)ISBN:- 9780387781679
- 363.7392
- 519.5 23
- 363.7392 628.53
- TD883.1
- 2009 E-423
- WA 950
Inhalte:
Zusammenfassung: Studies of Air Pollution and Health -- 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 Particulate Matter and Mortality in the United States.Zusammenfassung: 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. This book 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. The book 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 the book 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 the book 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. Roger D. Peng is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for spatial and temporal data. Dr. Peng is the author of numerous R packages and is a frequent contributor to the R mailing lists. Francesca Dominici is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She has published extensively on hierarchical and semiparametric modeling and has been the leader of major national studies of the health effects of air pollution. She has also participated in numerous panels conducted by the National Academy of Science assessing the health effects of environmental exposures and has consulted for the US Environmental Protection Agency's Clean Air Act Advisory Board.PPN: PPN: 164772581XPackage identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXMS | ZDB-2-SMA
CONTENTS; Preface; 1 Studies of Air Pollution and Health; 1.1 Introduction; 1.2 Time Series Studies; 1.3 Case-Crossover Studies; 1.4 Panel Studies; 1.5 Cohort Studies; 1.6 Design Comparisons; 2 Introduction to R and Air Pollution and Health Data; 2.1 Starting Up R; 2.2 The National Morbidity, Mortality, and Air Pollution Study; 2.3 Organization of the NMMAPSlite Package; 2.3.1 Reading city-specific data; 2.3.2 Pollutant data detrending; 2.3.3 Mortality age categories; 2.3.4 Metadata; 2.3.5 Configuration options; 2.4 MCAPS Data; 3 Reproducible Research Tools; 3.1 Introduction
3.2 Distributing Reproducible Research3.3 Getting Started; 3.4 Exploring a Cached Analysis; 3.5 Verifying a Cached Analysis; 3.6 Caching a Statistical Analysis; 3.7 Distributing a Cached Analysis; 3.8 Summary; 4 Statistical Issues in Estimating the Health Effectsof Spatial-Temporal Environmental Exposures; 4.1 Introduction; 4.2 Time-Varying Environmental Exposures; 4.3 Estimation Versus Prediction; 4.4 Semiparametric Models; 4.4.1 Overdispersion; 4.4.2 Representations for f; 4.4.3 Estimation of ß; 4.4.4 Choosing the degrees of freedom for f; 4.5 Combining Information and Hierarchical Models
5 Exploratory Data Analyses5.1 Introduction; 5.2 Exploring the Data: Basic Features and Properties; 5.2.1 Pollutant data; 5.2.2 Mortality data; 5.3 Exploratory Statistical Analysis; 5.3.1 Timescale decompositions; 5.3.2 Example: Timescale decompositions of PM10 and mortality; 5.3.3 Correlation at different timescales: A lookat the Chicago data; 5.3.4 Looking at more detailed timescales; 5.4 Exploring the Potential for Confounding Bias; 5.5 Summary; 5.6 Reproducibility Package; 5.7 Problems; 6 Statistical Models; 6.1 Introduction; 6.2 Models for Air Pollution and Health
6.3 Semiparametric Models6.3.1 GAMs in R; 6.4 Pollutants: The Exposure of Interest; 6.4.1 Single versus distributed lag; 6.4.2 Mortality displacement; 6.5 Modeling Measured Confounders; 6.6 Accounting for Unmeasured Confounders; 6.6.1 Using GAMs for air pollution and health; 6.6.2 Computing standard errors for parametricterms in GAMs; 6.6.3 Choosing degrees of freedom from the data; 6.6.4 Example: Semiparametric model for Detroit; 6.6.5 Smoothers; 6.7 Multisite Studies: Putting It All Together; 6.8 Summary; 6.9 Reproducibility Package; 6.10 Problems
7 Pooling Risks Across Locations and Quantifying SpatialHeterogeneity7.1 Hierarchical Models for Multisite Time Series Studiesof Air Pollution and Health; 7.1.1 Two-stage hierarchical model; 7.1.2 Three-stage hierarchical model; 7.1.3 Spatial correlation model; 7.1.4 Sensitivity analyses to the adjustment for confounders; 7.2 Example: Examining Sensitivity to Prior Distributions; 7.3 Reproducibility Package; 7.4 Problems; 8 A Reproducible Seasonal Analysis of Particulate Matterand Mortality in the United States; 8.1 Introduction; 8.2 Methods; 8.2.1 Combining information across cities
8.3 Results
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