000			04333nam a22003617a 4500
999			_c35428 _d35428
003			CUTN
005			20210708162947.0
008			210708b ||||| |||| 00| 0 eng d
020			_a9780387781662 (pbk.)
020			_a0387781668 (pbk.)
020			_a0387781676 (ebk.)
020			_a9780387781679 (ebk.)
041			_aEnglish
082	0	4	_a628.53 _222 _bPEN
100	1		_aPeng, Roger D.,
245	1	0	_aStatistical methods for environmental epidemiology with R : _ba case study in air pollution and health / _cRoger D. Peng, Francesca Dominici.
260			_aNew York ; _aLondon : _bSpringer, _cc2008.
300			_aix, 144 p. : _bill., 1 map ; _c23.5 cm.
440		0	_aUse R!
500			_aR.C.C PBK
505			_t1. Studies of air pollution and health. - _t2. Introduction to R and air pollution and health data. - _t3. Reproducible research tools. - _t4. Statistical issues in estimating the health effects of spatial-temporal environmental exposures. - _t5. Exploratory data analyses. - _t6. Statistical models. - _t7. Pooling risks across locations and quantifying spatial heterogeneity. - _t8. A reproducible seasonal analysis of Particulate Matter and mortaility in the U.S.
520			_aAdvances 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.
650		0	_aAir
650		0	_aEnvironmental health.
650		0	_aR (Computer program language)
690			_aDepartment of Epidemiology
700	1		_aDominici, Francesca.
942			_2ddc _cBOOKS
504			_aIncludes bibliographical references (p.[133]-142) and index.
650		0	_xPollution _xMathematical models.
906			_a7 _bcbc _ccopycat _d2 _eepcn _f20 _gy-gencatlg