1st Edition

Disease Mapping From Foundations to Multidimensional Modeling

    446 Pages
    by Chapman & Hall

    446 Pages 50 B/W Illustrations
    by Chapman & Hall

    446 Pages 50 B/W Illustrations
    by Chapman & Hall



    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.



    I. DISEASE MAPPING: THE FOUNDATIONS





    1. Introduction



    Some considerations on this book



    Notation





    2. Some basic ideas of Bayesian inference



    Bayesian inference



    Some useful probability distributions



    Bayesian Hierarchical Models



    Markov chain Monte Carlo Computing



    Convergence assessment of MCMC simulations





    3. Some essential tools for the practice of Bayesian disease mapping



    WinBUGS



    The BUGS language



    Running models in WinBUGS



    Calling WinBUGS from R



    INLA



    INLA basics



    Plotting maps in R



    Some interesting resources in R for disease mapping practitioners





    4. Disease mapping from foundations



    Why disease mapping?



    Risk measures in epidemiology



    Risk measures as statistical estimators



    Disease mapping, the statistical problem



    Non-spatial smoothing



    Spatial smoothing



    Spatial distributions



    The Intrinsic CAR distribution



    Some proper CAR distributions



    Spatial hierarchical models



    Prior choices in disease mapping models



    Some computational issues on the BYM model



    Some illustrative results on real data





    II. DISEASE MAPPING: TOWARDS MULTIDIMENSIONAL MODELING





    5. Ecological Regression



    Ecological regression: a motivation



    Ecological regression in practice



    Some issues to take care of in ecological regression studies



    Confounding



    Fallacies in ecological regression



    The Texas sharpshooter fallacy



    The ecological fallacy



    Some particular applications of ecological regression



    Spatially varying coefficients models



    Point source modelling





    6. Alternative spatial structures



    CAR-based spatial structures



    Geostatistical modeling



    Moving-average based spatial dependence



    Splines based modeling



    Modelling of specific features in disease mapping studies



    Modeling partitions and discontinuities



    Models for fitting zero excesses





    7. Spatio-temporal disease mapping



    Some general issues in spatio-temporal modelling



    Parametric temporal modelling



    Splines-based modelling



    Non-parametric temporal modelling





    8. Multivariate modelling



    Conditionally specified models



    Multivariate models as sets of conditional multivariate Distributions



    Multivariate models as sets of conditional univariate distributions



    Coregionalization models



    Factor models, Smoothed ANOVA and other approaches



    Factor models



    Smoothed ANOVA



    Other approaches





    9. Multidimensional modelling



    A brief introduction and review of multidimensional modeling



    A formal framework for multidimensional modeling



    Some tools and notation



    Separable modeling



    Inseparable modeling





    Annex 1





    Bibliography





    Index

    Biography

    Although Miguel A. Martinez-Beneito’s 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-Rocamora’s 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 Rocamora’s 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