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E-raamat: Disease Mapping: From Foundations to Multidimensional Modeling

, (FISABIO -Public Health, Valencia, Spain)
  • Formaat: 446 pages
  • Ilmumisaeg: 02-Jul-2019
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781482246421
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Disease Mapping: From Foundations to Multidimensional ModelingSuurem pilt
  • Formaat: 446 pages
  • Ilmumisaeg: 02-Jul-2019
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781482246421
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Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered.

Features:











Discusses the very latest developments on multivariate and multidimensional mapping.





Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches.





Balances epidemiological and statistical points-of-view.





Requires no previous knowledge of disease mapping.





Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets.





Supplies R code for the examples in the book so that they can be reproduced by the reader.

About the Authors:

Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master.

Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.

Arvustused

"Disease mapping, i.e. the study of the geographical distribution of diseases, is an important emerging tool not only for better understanding public health issues but also for deriving important clues for public health policy planners. This book is an effort by statisticians working as public health practitioners, whose careers have evolved surrounded by geographically referenced health data, to address issues related to this tool appropriately...As a great novelty of the book, the online material may enable readers to have direct access to most of the statistical/computing details that there is not enough room to fully explain within the book... In my opinion, researchers working in the area of population and public health in particular may find this book as a useful source to ensure optimal use of disease mapping. Further, since this book includes a fair number of examples, teachers of graduate-level courses on this topic may also find this book useful." - Sada Nand Dwivedi, ISCB News, July 2020

Preface xi
Authors xiii
I Disease mapping: The foundations
1(2)
1 Introduction
3(7)
1.1 Some considerations on this book
10(5)
1.1.1 Notation
13(2)
2 Some basic ideas of Bayesian inference
15(1)
2.1 Bayesian inference
15(10)
2.1.1 Some useful probability distributions
22(3)
2.2 Bayesian hierarchical models
25(11)
2.3 Markov Chain Monte Carlo computing
36(15)
2.3.1 Convergence assessment of MCMC simulations
41(10)
3 Some essential tools for the practice of Bayesian disease mapping
51(1)
3.1 WinBUGS
51(27)
3.1.1 The BUGS language
53(9)
3.1.2 Running models in WinBUGS
62(7)
3.1.3 Calling WinBUGS from R
69(9)
3.2 INLA
78(13)
3.2.1 INLA basics
81(10)
3.3 Plotting maps in R
91(7)
3.4 Some interesting resources in R for disease mapping practitioners
98(7)
4 Disease mapping from foundations
105(1)
4.1 Why disease mapping?
105(14)
4.1.1 Risk measures in epidemiology
106(4)
4.1.2 Risk measures as statistical estimators
110(5)
4.1.3 Disease mapping: the statistical problem
115(4)
4.2 Non-spatial smoothing
119(12)
4.3 Spatial smoothing
131(58)
4.3.1 Spatial distributions
132(4)
4.3.1.1 The Intrinsic CAR distribution
136(6)
4.3.1.2 Some proper CAR distributions
142(9)
4.3.2 Spatial hierarchical models
151(9)
4.3.2.1 Prior choices in disease mapping models
160(11)
4.3.2.2 Some computational issues on the BYM model
171(7)
4.3.2.3 Some illustrative results on real data
178(11)
II Disease mapping: Towards multidimensional modeling
189(2)
5 Ecological regression
191(1)
5.1 Ecological regression: a motivation
192(5)
5.2 Ecological regression in practice
197(9)
5.3 Some issues to take care of in ecological regression studies
206(17)
5.3.1 Confounding
206(10)
5.3.2 Fallacies in ecological regression
216(1)
5.3.2.1 The Texas sharpshooter fallacy
216(2)
5.3.2.2 The ecological fallacy
218(5)
5.4 Some particular applications of ecological regression
223(10)
5.4.1 Spatially varying coefficients models
223(3)
5.4.2 Point source modeling
226(7)
6 Alternative spatial structures
233(1)
6.1 CAR-based spatial structures
234(13)
6.2 Geostatistical modeling
247(5)
6.3 Moving-average based spatial dependence
252(4)
6.4 Spline-based modeling
256(9)
6.5 Modeling of specific features in disease mapping studies
265(16)
6.5.1 Modeling partitions and discontinuities
265(9)
6.5.2 Models for fitting zero excesses
274(7)
7 Spatio-temporal disease mapping
281(1)
7.1 Some general issues in spatio-temporal modeling
282(9)
7.2 Parametric temporal modeling
291(8)
7.3 Spline-based modeling
299(9)
7.4 Non-parametric temporal modeling
308(11)
8 Multivariate modeling
319(3)
8.1 Conditionally specified models
322(17)
8.1.1 Multivariate models as sets of conditional multivariate distributions
322(8)
8.1.2 Multivariate models as sets of conditional univariate distributions
330(9)
8.2 Coregionalization models
339(15)
8.3 Factor models, smoothed ANOVA and other approaches
354(15)
8.3.1 Factor models
356(3)
8.3.2 Smoothed ANOVA
359(6)
8.3.3 Other approaches
365(4)
9 Multidimensional modeling
369(1)
9.1 A brief introduction and review of multidimensional modeling
370(6)
9.2 A formal framework for multidimensional modeling
376(23)
9.2.1 Some tools and notation
377(3)
9.2.2 Separable modeling
380(3)
9.2.3 Inseparable modeling
383(16)
Appendix 1 399(2)
Bibliography 401(28)
Index 429
Although Miguel A. Martinez-Beneitos background is mostly based in mathematics/statistics his scientific career has been completely linked to Public Health. His first professional job was as statistician in the epidemiology unit of the Valencian regional health authority and all his research from then has been focused on the development of statistical methods for epidemiological studies. His main line of research is disease mapping and its extension to complex settings (multivariate spatial models, spatio-temporal models, spatial survival models, ) where he has published most of his research papers with either methodological/statistical or applied/epidemiological content. Regardless his peer-reviewed scientific publication Dr. Martinez-Beneito has been involved in several projects entailing the intensive application of disease mapping methods to the study of mortality in different contexts and regions. As a result he is author of 3 spatial atlas of mortality (2 of them corresponding to the Valencian region and another one to big Spanish cities) and 1 spatio-temporal atlas (http://www.geeitema.org/AtlasET/index.jsp?idioma=I). This extensive experience in geographical mortality studies makes Dr. Martinez-Beneito particularly suited to undertake this project.

Paloma Botella-Rocamoras background is based in mathematics/statistics, but her scientific career is mainly linked to statistics within Public Health. Her first scientific job was as part time research internship at the Epidemiology Unit of the Valencian regional health authority working in a project studying rare diseases, where she developed different spatial atlases of morbidity for rare diseases. During those years she also participated in the development of a spatial mortality atlas in the Valencian region, and a spatio-temporal mortality atlas in this same region (http://www.geeitema.org/AtlasET/index.jsp?idioma=I). She has also been the first author of the Spanish spatial atlas of rare diseases. She shared those jobs with her classes as part time associate professor at the University of Valencia and CEU-Cardenal Herrera University, where she already continues working as full time professor. Her teaching scope has always been linked to biostatistics in Health Sciences.

Following the topic of his doctoral thesis, Paloma Botella Rocamoras main line of research is disease mapping where she has published most of her research papers with either methodological/statistical or applied content. She has started to work in her recent scientific stay at the University of Minnesota (2013 summer) in the extension of disease mapping models to complex settings (multivariate spatial models, spatio-temporal models, ). This extensive experience in geographical mortality studies makes Dr. Botella-Rocamora particularly suited to undertake this project
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