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Lawson A.B., Browne W.J., Rodeiro C.L. — Disease Mapping with WinBUGS and MLwiN
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Название: Disease Mapping with WinBUGS and MLwiNАвторы: Lawson A.B., Browne W.J., Rodeiro C.L.Аннотация: Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages – such as WinBUGS and MLwiN – are now easy to implement in practice. Provides an introduction to Bayesian and multilevel modelling in disease mapping. Adopts a practical approach, with many detailed worked examples. Includes introductory material on WinBUGS and MLwiN. Discusses three applications in detail – relative risk estimation, focused clustering, and ecological analysis. Suitable for public health workers and epidemiologists with a sound statistical knowledge. Supported by a Website featuring data sets and WinBUGS and MLwiN programs. Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.
Язык: Рубрика: Математика /Вероятность /Статистика и приложения /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Год издания: 2003Количество страниц: 282Добавлена в каталог: 04.06.2005Операции: Положить на полку |
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Adaptive rejection sampling 24 41 47 96—97 At risk background 4 Autocorrelation function ACF 99 BACC 23 Bayes estimate 11 Bayes factor 26 Bayesian information criterion 26 Bayesian residual 27 Block kriging 210 Block updates 23 Boa 66 Bootstrapping 75 Brooks — Draper diagnostic 25 99 BYM model 123 125 Case control 7 Case-control design 158 Case-event analysis 2 Census tracts 2 Cluster detection 1 Coda 63 66 compound documents 48 Conditional autoregressive models, CAR 43 76 111 123 222 Conditional autoregressive models, car.I1 70 Conditional autoregressive models, car.normal 70 Conditional autoregressive models, car.proper 70 124 Conditional predictive ordinates 28 Confounder variables 6 7 Constant nodes 51 Control disease 158 Convergence 66 Convolution model 111 Correlated heterogeneity 10 Correlated heterogeneity, CH 167 Counties 2 Cross-validation 208 Cusum methods 2 5 DAG, directed acyclic graph 19 Dataset, East German lip cancer, 1980—1989 207 Dataset, European melanoma mortality, 1971—1980 77 Dataset, Falkirk respiratory cancer, 1978—1983 167 190—191 Dataset, Ohio respiratory cancer mortality, 1979—1988 116 128 180 Dataset, owa cancer registry survival data 235 Dataset, Scottish lip cancer 105 Dataset, South Carolina cancer mortality, 1998 69 Dataset, South Carolina congenital abnormality deaths 5 116 213 Dataset, South Carolina low birth weight, 2000 203 223 Dataset, South Carolina malignant neoplasm mortality, 1999 116 199 220 Dataset, total deaths in England and Wales 31 Deprivation indices 7 Deviance 62 DIC diagnostic 38 100 126 1 227 233 DIC diagnostic, deviance information criterion 26 47 Diffuse prior distributions 3 7 Disease clustering 1 Disease mapping 1 Doodlebugs 49 59 Ecological analysis 1 160 Ecological bias 197 Ecological fallacy 165 Ecological regression studies 199 EDA methods 6 Edge effects 251 EM algorithm 36 Empirical Bayes 11 Excel 77 Exposure modelling 160 External standardization 7 Extra binomial variation 227 Frailty 9 181 Frequentist approach 10 Full Bayes 11 Gauss — Hermite quadrature 15 39 Gaussian kriging models 71 Gelman — Rubin diagnostic 119 122 124 132 199 Gelman — Rubin statistic 25 63 130 Generalized additive models 6 Generalized linear mixed models 45 GeoBUGS 67 Geographically weighted regression GWR 207 232 Gibbs sampling 13 45 Gibbs Updates 23 96 Gibbs — Metropolis sampling 14 GIS, geographical information systems xvii Glm function 9 GLS estimation 36 Graphical models 51 Hidden process models 252 Hierarchical models 18 HLM package 30 37 Hybrid Metropolis — Gibbs algorithm 41 Hyperparameters 123 Hyperprior distribution 18 123 Intra-class correlation 31 Inverse Wishart prior distribution 152 Iterative generalized least squares estimation, IGLSE 36 109 Kernel regression 6 Laplace approximation 40 Laplace asymptotic integral approximation 13 Likelihood models 6 Linear Bayes methods 13 Log-concave posterior 24 Log-linear modelling 9 Log-normal model 120 Logical nodes 51 Map surveillance 252 Marginal likelihood 11 Marginal maximum likelihood 11 Marginal quasi-likelihood (MOL) algorithm 40 Markov chain 20—21 Markov chain Monte Carlo 27 46 94 Maximum a posteriori estimation 12 20 MCMC convergence 25 MCMC diagnostics 104 Metropolis updates 22 96 Metropolis — Hastings algorithm 14 21—22 Metropolis — Hastings updates 22 Mixture model 133 MLwiN macros 1 50 MLwiN worksheet 77 MOL estimation 87 90 107 146 Moran's I statistic 27 Multiple comparisons 158 Multiple-membership models 15 42 106 222 Multiple-membership multiple-classification models MMMC 43 Multiplicative expected risk 4 Multivariate normal distribution 3 6 Multivariate, CAR models 252 Multivariate, disease mapping 251 Nearest neighbour sets 216 Nonparametric maximum likelihood 13 Nonspecific heterogeneity 10 Overdispersion 9 Parametric bootstrap 2 7 Partial autocorrelation function, PACF 99 Partition models 6 Point process models 2 252 Poisson distribution 7 Poisson process models 6 252 Poisson regression 8 85—86 101 Poisson-gamma model 11 117 118 POL algorithm 40 90 97 108 Post code 2 Post hoc analysis 158 Posterior approximation 11 Posterior distribution 11 17 Posterior expectation 11 Posterior inference 19 Posterior predictive distribution 28 Posterior sampling 12 Prior distributions 17 Prospective study 156 Public health surveillance 1 Putative source of hazard 1 Quantile-quantile plots 25 Quasi-likelihood estimates 40 Quasi-likelihood methods 43 98 231 Raftery — Lewis diagnostic 25 99 Random coefficient model 33 Random effects 9 11 Random intercepts model 33 Random slopes model 33 Random walk 181 Rectangular format 5 7 Relative risk 1 4 8 Restricted iterative generalized least squares, RIGLS 36 Restricted maximum likelihood, REML 36 75 Retrospective study 156 S-Plus format 57 Saturated estimate 5 Saturated ML estimator 7 Scripts 64 Sequential Monte Carlo estimation 252 Simple kriging 214 Slice sampling 24 Space-time interaction 185 Space-time models 128 Sparseness 9 Spatial autocorrelation 10 15 Spatial prediction 137 Spatial survival analysis 235 Spatial trend 7 Specific heterogeneity 10 Standardized mortality/morbidity ratio, SMR 5 31 116 Standardized mortality/morbidity ratio, variance 5 Stochastic nodes 51 Tract centroid 2 4 Tract count analysis 2 Uncorrelated heterogeneity, UH 10 167 Univariate random walk Metropolis sampling 41 Unobserved heterogeneity 162 Uranium field 213 VARCL 30 36 Variance components model 31 zip code 2
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