Bayesian disease mapping: Hierarchical modeling in spatial epidemiology

Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology

Volumn , Issue , 2008, Pages 1-344

  Lawson, Andrew B ^a  

^a MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

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EID: 85055926533     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (262)

1. Agarwal, D., A. Gelfand, and S. Citron-Pousty (2002). Zero-inflated models with application to spatial count data. Environmental and Ecological Statistics 9,341-355.
2. Anto, J. and J. Sunyer (1990). Epidemiological studies of asthma epidemics in Barcelona. Chest 98,185-190.
3. Arya, S. P. (1998). Air Pollution Metereology and Dispersion.New York: Oxford University Press.
4. Baddeley, A. and R. Turner (2000). Practical maximum pseudolikelihood for spatial point patterns. Australia and New Zealand Journal of Statistics 42, 283-322.
5. Baddeley, A., R. Turner, J. Mpller, and M. Hazelton (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society B 67, 617-666, with discussion.
6. Balakrishnan, S. and D. Madigan (2006). Algorithms for sparse linear classifiers in the massive data setting. Journal of Machine Learning Research 1.
7. Banerjee, S. and B. P. Carlin (2003). Semiparametricspatio-temporal frailty modeling. Environmetrics 14,523-535.
8. Banerjee, S., B. P. Carlin, and A. E. Gelfand (2004). Hierarchical Modeling and Analysis for Spatial Data.London: Chapman and Hall/CRC Press.
9. Banerjee, S., M. M. Wall, and B. P. Carlin (2003). Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics 4, 123-142.
10. Bankman, I. (Ed.) (2007). Handbook of Medical Image Processing and Analysis(2nd ed.), Volume 1. New York: Academic Press.
11. Barndorff-Nielsen, O. E., W. S. Kendall, and M. N. M. van Lieshout (1999). Stochastic Geometry: Likelihood and Computation.Boca Raton, FL: Chapman &Hall.
12. Bastos, L. and D. Gamerman (2006). Dynamical survival models with spatial frailty. Lifetime Data Analysis 12,441-460.
13. Berman, M. and T. R. Turner (1992). Approximating point process likelihoods with GLIM. Applied Statistics 41,31-38.
14. Bernardinelli, L., D. G. Clayton, C. Pascutto, C. Montomoli, M. Ghislandi, and M. Songini (1995). Bayesian analysis of space-time variation in disease risk. Statistics in Medicine 14,2433-2443.
15. Bernardo, J. M. and A. F. M. Smith (1994). Bayesian Theory.New York: Wiley
16. Besag, J. (1975). Statistical analysis of non-lattice data. The Statistician 24, 179-195.
17. Besag, J. and P. J. Green (1993). Spatial statistics and Bayesian computation. Journal of the Royal Statistical Society, Series B 55,25-37.
18. Besag, J. and J. Newell (1991). The detection of clusters in rare diseases. Journal of the Royal Statistical Society, Series A 154,143-155.
19. Besag, J. and J. Tantrum (2003). Likelihood analysis of binary data in space and time. In P. Green, N. Hjort, and S. Richardson (Eds.), Highly Structured Stochastic Systems, Chapter 9A, 289-295. New York: Oxford University Press.
20. Besag, J., J. York, and A. Mollie (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43,1-59.
21. Best, N., S. Richardson, and A. Thomson (2005). A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research 14, 35-59.
22. Best, N. G. and J. C. Wakefield (1999). Accounting for inaccuracies in population counts and case registration in cancer mapping studies. Journal of the Royal Statistical Society A 162,363-382.
23. Bishop, C. (2006). Pattern Recognition and Machine Learning.New York: Springer.
24. Boehning, D., E. Dietz, and P. Schlattmann (2000). Space-time mixture modelling of public health data. Statistics in Medicine 19,2333-2344.
25. Boehning, D., E. Dietz, P. Schlattmann, L. Mendonca, and U. Kirchner (1999). The zero-inflated poisson model and the decayed, missing and filled teeth index in dental epidemiology. Journal of the Royal Statistical Society A 162, 195-209.
26. Bottai, M., M. Geraci, and A. Lawson (2007). Testing for unusual aggregation of health risk in semiparametric models. Statistics in Medicine. Doi: 10.1002/sim.3126.
27. Bowman, A. and A. Azzalini (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations.New York: Oxford University Press.
28. Breslow, N. and D. G. Clayton (1993). Approximate inference in generalised linear mixed models. Journal of the American Statistical Association 88, 9-25.
29. Brix, A. and P. Diggle (2001). Spatio-temporal prediction for log-gaussian cox processes. Journal of the Royal Statistical Society B 63,823-841.
30. Brooks, S. and A. E. Gelman (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics7, 434-455.
31. Brooks, S., P. Guidici, and A. Philippe (2003). Nonparamteric convergence assessment for mcmc model selection. Journal of Computational and Graphical Statistics 12,1-22.
32. Brooks, S. P. (1998). Quantitative convergence assessment for MCMC via Cusums. Statistics and Computing 8,267-274.
33. Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas (2001). Introduction to Distance Sampling.New York: Oxford University Press.
34. Byers, S. and A. Raftery (2002). Bayesian estimation and segmentation of spatial point processes using voronoitilings. In A. B. Lawson and D. Denison (Eds.), Spatial Cluster Modelling, Chapter 6. New York: CRC Press.
35. Cai, B. and D. Dunson (2008). Bayesian variable selection in nonparametric random effects models. submitted.
36. Cai, B. and A. B. Lawson (2008). Space-time latent structure models with gam-like covariate linkage. submitted.
37. Caragea, P. and M. Kaiser (2006). Covariates and time in the autologistic model. Technical Report 2006-19, Iowa State University, Department of Statistics.
38. Carlin, B. P. and J. Hodges (1999). Hierarchical proportional hazards regression models for highly stratified data. Biometrics 55,1162-1170.
39. Carlin, B. P. and T. Louis (2000). Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). New York: Chapman &Hall/CRC Press.
40. Carroll, R., D. Ruppert, L. Stefanski, and C. Crainiceanu (2006). Measurement Error in Nonlinear Models (2nd ed.). New York: Chapman &Hall.
41. Cassella, G. and E. I. George (1992). Explaining the Gibbs Sampler. The American Statistician 46,167-174.
42. Chaix, B., M. Rosvall, J. Lynch, and J. Merlo (2006). Disentangling contextual effects on cause specific mortality in a longitudinal 23-year follow-up study: impact of population density or socio-economic environment?International Journal of Epidemiology 35,633-643.
43. Clark, A. B. and A. B. Lawson (2002). Spatio-temporal cluster modelling of small area health data. In A. B. Lawson and D. Denison (Eds.), Spatial Cluster Modelling, Chapter 14, 235-258. New York: CRC Press.
44. Clayton, D. G. and L. Bernardinelli (1992). Bayesian methods for mapping disease risk. In P. Elliott, J. Cuzick, D. English, and R. Stern (Eds.), Geographical and Environmental Epidemiology: Methods for Small-Area Studies. Oxford: Oxford University Press.
45. Clayton, D. G., L. Bernardinelli, and C. Montomoli (1993). Spatial correlation in ecological analysis. International Journal of Epidemiology 22,1193-1202.
46. Clayton, D. G. and J. Kaldor (1987). Empirical Bayes estimates of age-standardised relative risks for use in disease mapping. Biometrics 43,671-691.
47. Cliff, A. D. and J. K. Ord (1981). Spatial Processes: Models and Applications. London: Pion.
48. Congdon, P. (2003). Applied Bayesian Modelling.New York: Wiley.
49. Congdon, P. (2005). Bayesian Models for Categorical Data.New York: Wiley.
50. Congdon, P. (2007). Bayesian Statistical Modelling (2nd ed.). New York: Wiley.
51. Congdon, P., M. Almog, S. Curtis, and R. Ellerman (2007). A spatial structural equation modelling framework for health count responses. Statistics in Medicine 26, 5267-5284.
52. Cook, R. and J. Lawless (2007). The Statistical Analysis of Recurrent Events. New York: Springer.
53. Cooner, F., S. Banerjee, and M. McBean (2006). Modelling geographically referenced survival data with a cure fraction. Statistical Methods in Medical Research 15(4), 307-324.
54. Cowles, M. K. (2004). Review of WinBUGS 1.4. The American Statistician 58, 330-336.
55. Cressie, N., S. Richardson, and I. Jaussent (2004). Ecological bias: use of maximum-entropy approximations. Australia New Zealand Journal of Statistics 46,233-255.
56. Cressie, N. A. C. (1993). Statistics for Spatial Data(revised ed.). New York: Wiley.
57. Cressie, N. A. C. (1996). Change of support and the modifiable areal unit problem. Geographical Systems 2,83-101.
58. Cressie, N. A. C. and A. Mugglin (2000). Spatio-temporal hierarchical modelling of an infectious disease from (simulated) count data. In J. Bethlehem and P. van der Heijden (Eds.), Compstat 2000.Heidelberg: Physicaverlag.
59. Cressie, N. C. and A. Lawson (2000). Hierarchical probability models and Bayesian analysis of mine locations. Advances in Applied Probability 32(2), 315-330.
60. Dabney, A. and J. Wakefield (2005). Issues in the mapping of two diseases. Statistical Methods in Medical Research 14,83-112.
61. Davison, A. C. and D. V. Hinkley (1997). Bootstrap Methods and their Application.London: Cambridge University Press.
62. Dellaportas, P., J. Forster, and I. Ntzoufras (2002). On Bayesian model and variable selection using mcmc.Statistics and Computing 12,27-36.
63. Denison, D., N. Adams, C. Holmes, and D. Hand (2002). Bayesian partition modelling. Computational Statistics and Data Analysis 38, 475-485.
64. Denison, D. and C. Holmes (2001). Bayesian partitioning for estimating disease risk. Biometrics 57,143-149.
65. Denison, D., C. Holmes, B. Mallick, and A. Smith (2002). Bayesian Methods for nonlinear Classification and Regression.New York: Wiley.
66. Diggle, P. (2007). Spatio-temporal point processes: Methods and applications. In B. Finkenstadt, L. Held, and V. Isham (Eds.), Statistical Methods for Spatio-Temporal Systems, Chapter 1, 1-45. London: CRC Press.
67. Diggle, P. and P. Elliott (1995). Statistical issues in the analysis of disease risk near point sources using individual or spatially aggregated data. Journal of Epidemiology and Community Health 49, s20-s27.
68. Diggle, P., T. Fiksel, Y. Ogata, D. Stoyan, and M. Tanemura (1994). On parameter estimation for pairwise interaction processes. International Statistical Review 62,99-117.
69. Diggle, P., P. Heagerty, K.-Y. Liang, and S. Zeger (2002). Analysis of Longitudinal Data(2 ed.). New York: Oxford University Press.
70. Diggle, P., S. Morris, P. Elliott, and G. Shaddick (1997). Regression modelling of disease risk in relation to point sources. Journal of the Royal Statistical Society, A 160,491-505.
71. Diggle, P. and P. Ribeiro Jr. (2007). Model-based Geostatistics.New York: Springer.
72. Diggle, P. and B. Rowlingson (1994). A conditional approach to point process modelling of elevated risk. Journal of the Royal Statistical Society A 157, 433-440.
73. Diggle, P., P. Zheng, and P. Durr (2005). Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in cornwall, UK. Journal of the Royal Statistical Society C 54,645-658.
74. Diggle, P. J. (1985). A kernel method for smoothing point process data. Journal of the Royal Statistical Society C 34,138-147.
75. Diggle, P. J. (1990). A point process modelling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point. Jour. Royal Statist. Soc. A 153,349-362.
76. Diggle, P. J. (2003). Statistical Analysis of Spatial Point Patterns (2nd ed.). London: Arnold.
77. Diggle, P. J. (2005). Spatio-temporal point processes, partial likelihood, foot and mouth disease. Statistical Methods in Medical Research 15,325-336.
78. Diggle, P. J., S. Morris, and J. C. Wakefield (2000). Point-source modelling using matched case-control data. Biostatistics 1, 1-17.
79. Diggle, P. J., J. Tawn, and R. Moyeed (1998). Model-based Geostatistics. Journal of the Royal Statistical Society C 47,299-350.
80. Duan, J., M. Guidani, and A. Gelfand (2007). Generalized spatial dirichlet process models. Biometrika 94,809-825.
81. Durr, P. and A. Gatrell (Eds.) (2004). GIS and Spatial Analysis in Veterinary Science.London: Cabi.
82. Durr, P., N. Tait, and A. B. Lawson (2005). Bayesian hierarchical modelling to enhance the epidemiological value of abattoir surveys for bovine fasciolosis. Preventive Veterinary Medicine 71,157-172.
83. Eberley, L. and B. P. Carlin (2000). Identifiability and convergence issues for Markov chain Monte Carlo fitting of spatial models. Statistics in Medicine 19,2279-2294.
84. Elliott, P., J. Cuzick, D. English, and R. Stern (Eds.) (1992). Geographical and Environmental Epidemiology:Methods for Small-Area Studies. Oxford University Press.
85. Elliott, P., J. C. Wakefield, N. G. Best, and D. J. Briggs (Eds.) (2000). Spatial Epidemiology: Methods and Applications.London: Oxford University Press.
86. Esman, N. A. and G. M. Marsh (1996). Applications and limitations of air dispersion modeling in environmental epidemiology. Journal of Exposure Analysis and Environmental Epidemiology 6, 339-353.
87. Faraway, J. (2006). Extending the Linear Model with R.New York: Chapman &Hall/CRC.
88. Farnsworth, M., J. Hoeting, N. Hobbs, and M. Miller (2006). Linking chronic wasting disease to mule deer movement: a hiererchical Bayesian approach. Ecological Applications 16,1026-1036.
89. Fernandez, C. and P. Green (2002). Modelling spatially correlated data via mixtures: a Bayesian approach. Journal of the Royal Statitsical Society B 64,805-826.
90. Ferreira, J., D. Denison, and C. Holmes (2002). Partition modelling. In A. B. Lawson and D. Denison (Eds.), Spatial Cluster Modelling, Chapter 7, 125-145. New York: CRC Press.
91. Fotheringham, A. S., C. Brunsdon, and M. Charlton (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships.New York: Wiley.
92. French, J. and M. Wand (2004). Generalized additive models for cancer mapping with incomplete covariates. Biostatistics 5,177-191.
93. Gamerman, D. and H. Lopes (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference.New York: CRC Press.
94. Gangnon, R. (2006). Impact of prior choice on local Bayes factors for cluster detection. Statistics in Medicine 25,883-895.
95. Gangnon, R. and M. Clayton (2000). Bayesian detection and modeling of spatial disease clustering. Biometrics 56, 922-935.
96. Gardner, M. J. (1989). Review of reported increases of childhood cancer rates in the vicinity of nuclear installations in the UK. Journal of the Royal Statistical Society 152,307-325.
97. Gelfand, A. and S. Ghosh (1998). Model choice: A minimum posterior predictive loss approach. Biometrika 85,1-11.
98. Gelfand, A., A. Kottas, and S. MacEachern (2005). Bayesian nonparametric spatial modeling with dirichlet process mixing. Journal of the American Statistical Association 100,1021-1035.
99. Gelfand, A. and P. Vounatsou (2003). Proper multivariate conditional autoregressive models for spatial data. Biostatistics 4,11-25.
100. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1,515-533.
101. Gelman, A., J. B. Carlin, H. S. Stern, and D. Rubin (2004). Bayesian Data Analysis.London: Chapman &Hall/CRC press.
102. Gelman, A. and J. Hill (2007). Data Analysis Using Regression and Multidevel-Hierarchical Models. New York: Cambridge University Press.
103. Gelman, A. E. and D. Rubin (1992). Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7,457-511.
104. Geweke, J. (1992). Evaluating the accuracy of sampling -based approaches to the calculation of posterior moments. In J. Bernardo, J. Berger, A. Dawid, and A. Smith (Eds.), Bayesian Statistics4. New York: Oxford University Press.
105. Ghosh, M. and J. N. K. Rao (1994). Small area estimation: an appraisal. Statistical Science 9, 55-93.
106. Ghosh, S., P. Mukhopadhyay, and J.-C. Lu (2006). Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference 136,1360-1375.
107. Gilks, W. R., D. G. Clayton, D. J. Spiegelhalter, N. G. Best, A. J. McNeil, L. D. Sharples, and A. J. Kirby (1993). Modelling complexity: Applications of Gibbs Sampling in medicine. Journal of the Royal Statistical Society B 55,39-52.
108. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter (Eds.) (1996). Markov Chain Monte Carlo in Practice.London: Chapman &Hall.
109. Gneiting, T., M. Genton, and P. Guttorp (2007). Geostatistical space-time models, stationarity, separability, and full symmetry. In B. Finkenstadt, L. Held, and V. Isham (Eds.), Statistical Methods for Spatio-Temporal Systems, Chapter 4, pp. 151-175. London: CRC Press.
110. Godsill, S. (2001). On the relationship between Markov chain Monte Carlo methods for model uncertainty. Journal of Computational and Graphical Statistics 10,230-248.
111. Goldstein, H. and A. Leyland (2001). Further topics in multilevel modelling: Context and composition. In A. Leyland and H. Goldstein (Eds.), Multilevel Modelling of Health Statistics, Chapter 12.4, 181-186. New York: Wiley.
112. Greco, F., A. B. Lawson, D. Cocchi, and T. Temples (2005). Voronoitesse-lation and other methods for misaligned small area health data. Environmental and Ecological Statistics 12,379-395.
113. Green, P. J. (1995). Reversible jump MCMC computation and Bayesian model determination.Biometrika 82,711-732.
114. Green, P. J. and S. Richardson (2002). Hidden Markov models and disease mapping. Journal of the American Statitsical Association 97,1055-1070.
115. Greenland, S. (1992). Divergent biases in ecologic and individual-level studies. Statistics in Medicine 11,1209-1223.
116. Greenland, S. and J. Robins (1994). Ecologic studies—biases, misconceptions and counterexamples. American Journal of Sociology 139,747-759.
117. Griffin, J. and M. Steel (2006). Order-based dependent Dirichlet processes. Journal of the American Statistical Asociation 101,179-194.
118. Gustafson, P. (1998). Flexible Bayesian modelling for survival data. Lifetime Data Analysis 4, 281-299.
119. Gustafson, P. (2004). Measurement Error and Misclassification in Statistics and Epidemiology.London: Chapman &Hall.
120. Heagerty, P. and S. Lele (1998). A composite likelihood approach to binary spatial data. Journal of the American Statistical Association 93,1099-1111.
121. Hegarty, A. and D. Barry (2008). Bayesian disease mapping using product partition models. Statistics in Medicine (to appear).
122. Heisterkamp, S., G. Doornbos, and N. Nagelkerke (2000). Assessing health impact of environmental pollution sources using space-time models. Statistics in Medicmc 19,2569-2578.
123. Held, L., I. Natario, S. Fenton, H. Rue, and N. Becker (2005). Towards joint disease mapping. Statistical Methods in Medical Research 14, 61-82.
124. Henderson, R., S. Shimakura, and D. Gorst (2002). Modeling spatial variation in leukemia survival data. Journal of the American Statistical Association 97,965-972.
125. Hohle, M., E. Jorgensen, and P. O’Neill (2005). Inference in disease transmission experiments by using stochastic epidemic models. Journal of the Royal Statistical Society C 54,349-366.
126. Hossain, M. and A. Lawson (2008). Approximate methods in Bayesian point process spatial models. submitted.
127. Hossain, M. and A. B. Lawson (2005). Local likelihood disease clustering: Development and evaluation. Environmental and Ecological Statistics 12, 259-273.
128. Hossain, M. and A. B. Lawson (2006). Cluster detection diagnostics for small area health data: with reference to evaluation of local likelihood models. Statistics in Medicine 25,771-786.
129. Ibrahaim, J., M. Chen, and D. Sinha (2000). Bayesian Survival Analysis.New York: Springer.
130. Inskip, H., V. Beral, P. Fraser, and P. Haskey (1983). Methods for age-adjustment of rates. Statistics in Medicine 2,483-493.
131. Ishwaran, H. and L. James (2001). Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association 96,161-173.
132. Ishwaran, H. and L. James (2002). Approximate Dirichlet process computing in finite normal mixtures: Smoothing and prior information. Journal of Computational and Graphical Statistics 11,1-26.
133. Jin, X., S. Banerjee, and B. Carlin (2008). Order-free coregionalized areal data models with application to multiple disease mapping. Journal of the Royal Statistical Society B. (to appear).
134. Jin, X., B. Carlin, and S. Banerjee (2005). Generalized hierarchical multivariate car models for areal data. Biometrics 61,950-961.
135. Kauermann, G. and J. D. Opsomer (2003). Local likelihood estimation in generalized additive models. Scandinavian Journal of Statistics 30,317-337.
136. Kelsall, J. and P. Diggle (1998). Spatial variation in risk of disease: a non-parametric binary regression approach. Applied Statistics 47,559-573.
137. Kelsall, J. and J. Wakefield (2002). Modelling spatial variation in disease risk: A geostatistical approach. Journal of the American Statitsical Association 97,692-701.
138. Kim, H., D. Sun (2001). A bivariate bayes method for improving the estimates of mortiality rates with a twofold autoregressive model. Journal of the American Statistical Association 96,1506-1521.
139. Kim, S., M. Tadesse, and M. Vanucci (2006). Variable selection in clustering via Dirichlet process mixture models. Biometrika 93, 877-893.
140. Knorr-Held, L. (2000). Bayesian modelling of inseparable space-time variation in disease risk. Statistics in Medicine 19,2555-2567.
141. Knorr-Held, L. and J. Besag (1998). Modelling risk from a disease in time and space. Statistics in Medicine 17,2045-2060.
142. Knorr-Held, L. and N. G. Best (2001). A shared component model for detecting joint and selective clustering of two diseases. Journal of the Royal Statistical Society 164,73-85.
143. Knorr-Held, L. and G. Rasser (2000). Bayesian detection of clusters and discontinuities in disease maps. Biometrics 56,13-21.
144. Knorr-Held, L. and S. Richardson (2003). A hierarchical model for space-time surveillance data on meningococcal disease incidence. Journal of the Royal Statistical Society 52, 169-183.
145. Kottas, A., J. Duan, and A. Gelfand (2007). Modelling disease incidence data with spatial and spatio-temporal dirichlet process mixtures.Biometrical Journal 49(5), 1-14.
146. Kulldorff, M. and N. Nagarwalla (1995). Spatial disease clusters: detection and inference. Statistics in Medicine 14,799-810.
147. Kunsch, H. (1987). Intrinsic autoregressions and related models on the twodimensional lattice. Biometrika 74,517-524.
148. Kuo, L. and B. Mallick (1998). Variable selection for regression models. Sankhya 60, 65-81.
149. Lachin, J. L. (2000). Statistical considerations in the intent-to-treat principle. Controlled Clinical Trials 21,167-189.
150. Lambert, D. (1992). Zero-inflated poisson regression, with application to defects in manufacturing. Technometrics 34, 1-14.
151. Lambert, P., A. Sutton, P. Burton, K. Abrams, and P. Jones (2005). How vague is vague? A simulation study of the impact of the use of vague prior distributions in mcmc using winbugs. Statistics in Medicine 24,2401-2428.
152. Lawson, A. B. (1992a). GLIM and normalising constant models in spatial and directional data analysis. Computational Statistics and Data Analysis 13, 331-348.
153. Lawson, A. B. (1992b). On fitting non-stationary markov point processes on glim. In Y. Dodge and J. Whittacker (Eds.), Computational Statistics I. PhysicaVerlag.
154. Lawson, A. B. (1993a). A deviance residual for heterogeneous spatial Poisson processes. Biometrics 49,889-897.
155. Lawson, A. B. (1993b). On the analysis of mortality events around a prespecified fixed point. Journal of the Royal Statistical Society A 156,363-377.
156. Lawson, A. B. (1995). Markov chain Monte Carlo methods for putative pollution source problems in environmental epidemiology. Statistics in Medicine 14, 2473-2486.
157. Lawson, A. B. (1996). Use of deprivation indices in small area studies: letter. Journal of Epidemiology and Community Health 50,689-690.
158. Lawson, A. B. (2000). Cluster modelling of disease incidence via rjmcmc methods: a comparative evaluation. Statistics in Medicine 19,2361-2376.
159. Lawson, A. B. (2001). Statistical Methods in Spatial Epidemiology.New York: Wiley.
160. Lawson, A. B. (2002). The analysis of putative sources of health hazard. In D. Zimmerman (Ed.), Encyclopedia of Life Support Systems.UNESCO.
161. Lawson, A. B. (2004). Some issues in the spatio-temporal analysis of public health surveillance data. In R. Brookmeyer and D. Stroup (Eds.), Monitoring the Health of Populations: Statistical Principles and Methods for Public Health Surveillance, Chapter 11, pp. 289-314. New York: Oxford University Press.
162. Lawson, A. B. (2006a). Disease cluster detection: a critique and a Bayesian proposal. Statistics in Medicine 25,897-916.
163. Lawson, A. B. (2006b). Statistical Methods in Spatial Epidemiology (2nd ed.). New York: Wiley.
164. Lawson, A. B. and S. Banerjee (2008). Bayesian spatial analysis. In S. Fother-ingham and P. Rogerson (Eds.), Handbook of Spatial Analysis, Chapter 9. New York: Sage.
165. Lawson, A. B., A. Biggeri, D. Boehning, E. Lesaffre, J.-F. Viel, A. Clark, P. Schlattmann, and F. Divino (2000). Disease mapping models: An empirical evaluation. Statistics in Medicine 19,2217-2242. special issue: Disease Mapping with emphasis on evaluation of methods.
166. Lawson, A. B., A. Biggeri, and E. Dreassi (1999). Edge effects in disease mapping. In A. B. Lawson, D. B’ohning, E. Lesaffre, A. Biggeri, J.-F. Viel, and R. Bertollini (Eds.), Disease Mapping and Risk assessment for Public Health, Chapter 6, 85-97. New York: Wiley.
167. Lawson, A. B., D. Bohning, E. Lessafre, A. Biggeri, J. F. Viel, and R. Bertollini (Eds.) (1999). Disease Mapping and Risk Assessment for Public Health. Chichester: Wiley.
168. Lawson, A. B., W. J. Browne, and C. L. Vidal-Rodiero (2003). Disease Mapping with WinBUGS and MLwiN.New York: Wiley.
169. Lawson, A. B. and A. Clark (1999a). Markov chain Monte Carlo methods for clustering in case event and count data in spatial epidemiology. In M. E. Halloran and D. Berry (Eds.), Statistics and Epidemiology: Environment and Clinical Trials, pp. 193-218. New York: Springer Verlag.
170. Lawson, A. B. and A. Clark (1999b). Markov chain Monte Carlo methods for putative sources of hazard and general clustering. In A. B. Lawson, D. Bohning, E. Lesaffre, A. Biggeri, J.-F. Viel, and R. Bertollini (Eds.), Disease Mapping and Risk Assessment for Public Health, Chapter 9, 119-141. Wiley.
171. Lawson, A. B. and A. Clark (2002). Spatial mixture relative risk models applied to disease mapping. Statitsics in Medicine 21,359-370.
172. Lawson, A. B. and D. Denison (2002). Spatial cluster modelling: an overview. In A. B. Lawson and D. Denison (Eds.), Spatial Cluster Modelling, Chapter 1, pp. 1-19. New York: CRC press.
173. Lawson, A. B. and P. Leimich (2000). Approaches to space-time modelling of infectious disease behaviour. IMA journal of Mathematics applied to Medicine and Biology 17,1-13.
174. Lawson, A. B. and J.-F. Viel (1995). Tests for directional space-time interaction in epidemiological data. Statistics in Medicine 14,2383-2392.
175. Lawson, A. B. and F. Williams (2000). Spatial competing risk models in disease mapping. Statistics in Medicine 19,2451-2468.
176. Lawson, A. B. and F. L. R. Williams (2001). An Introductory Guide to Disease Mapping. New York: Wiley.
177. Lawson, A. B. and H. Zhou (2005). Spatial statistical modeling of disease outbreaks: with particular reference to the UK FMD epidemic of 2001. Preventive Veterinary Medicine 71,141-156.
178. Leonard, T. and H. Hsu (1999). Bayesian Methods.London: Cambridge University Press.
179. Leyland, A. and H. Goldstein (Eds.) (2001). Multilevel Modelling in Health Statistics. London: Wiley.
180. Lindsay, B. G. (1988). Composite likelihood methods. Contemporary Mathematics 80, 221-238.
181. Little, R. and D. Rubin (2002). Statistical Analysis with Missing Data.New York: Wiley.
182. Liu, J. S. (2001). Monte Carlo Strategies in Scientific Computing.New York: Springer.
183. Liu, X., M. Wall, and J. Hodges (2005). Generalised spatial structural equation models. Biostatistics 6,539-557.
184. Louie, M. M. and E. D. Kolaczyk (2006). Multiscaledetction of localised anomalous structure in aggregate disease incidence data. Statistics in Medicine 25, 787-810.
185. Lu, H. and B. Carlin (2005). Bayesian areal wombling for geographical boundary analysis. Geographical Analysis 37,265-285.
186. Ma, B., A. B. Lawson, and Y. Liu (2007). Evaluation of Bayesian models for focused clustering in health data. Environmetrics 18,1-16.
187. Ma, H., B. Carlin, and S. Banerjee (2006). Hierarchical and joint site edge methods for medicare hospice service region boudary analysis. submitted.
188. Macnab, Y. (2007). Spline smoothing in Bayesian disease mapping. Environmetrics 18,727-744.
189. Maheswaran, R. and M. Craglia (Eds.) (2004). GIS in Public Health Practice. New York: CRC press.
190. Maindonald, J. and J. Braun (2003). Data Analysis and Graphics Using R. New York: Cambridge University Press.
191. Marin, J.-M. and C. Robert (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics.New York: Springer.
192. McCullagh, P. and J. Nelder (1989). Generalised Linear Models (2nd ed.). London: Chapman &Hall.
193. McKeague, I. and M. Loiseaux (2002). Perfect sampling for point process cluster modelling. In A. B. Lawson and D. Denison (Eds.), Spatial Cluster-Modelling, Chapter 5. New York: CRC Press.
194. Mira, A., J. Moller, and G. Roberts (2001). Perfect slice samplers. Journal of the Royal Statistical Society B 63,583-606.
195. Molenberghs, G. and G. Verbeke (2005). Models for Discrete Longitudinal Data.New York: Springer.
196. Moller, J. and O. Skare (2001). Colouredvoronoitesselations for Bayesian image analysis and reservoir modelling. Statistical Modelling 1,213-232.
197. Mpller, J., A. Syversveen, and R. P. Waagepetersen (1998). Log Gaussian Cox processes. Scandinavian Journal of Statistics 25,451-482.
198. Moller, J. and R. Waagepetersen (1998). Markov connected component fields. Advances in Applied Probability 30,1-35.
199. Moller, J. and R. Waagpetersen (2004). Statistical Inference and Simulation for Spatial Point Processes.New York: CRC Press.
200. Morgan, O., M. Vreiheid, and H. Dolk (2004). Risk of low birth weight near eurohazcon hazardous waste landfill sites in england. Archives of Environmental Health 59,149-151.
201. Morton, A. and B. Finkenstadt (2005). Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods. Journal of the Royal Statistical Society C 54,575-594.
202. Mugglin, A., N. Cressie, and I. Gemmel (2002). Hierarchical statistical modelling of influenza epidemic dynamics in space and time. Statitsics in Medicme 21,2703-2721.
203. Neal, P. and G. Roberts (2004). Statistical inference and model selection for the 1861 hagelloch measles epidemic. Biostatistics 5, 249-261.
204. Neal, R. M. (2003). Slice sampling. Annals of Statistics 31,1-34.
205. Nott, D. J. and T. Ryden (1999). Pairwise likelihood methods for inference in image models. Biometrika 86,661-676.
206. Oden, N., R. Sokal, M. Fortin, and H. Goebl (1993). Categorical wombling: Detecting regions of significant change in spatially located categorical variables. Geographical Analysis 25(4).
207. O’Loughlin, J. (2002). The Electoral Geography of Weimar Germany: Exploratory Spatial Data Analyses (ESDA) of Protestant Support for the Nazi Party. Political Analysis 10(3), 217-243.
208. Pfeiffer, D. U., T. P. Robinson, M. Stevenson, K. B. Stevens, D. J. Rogers, and A. C. A. Clements (Eds.) (2008). Spatial Analysis in Epidemiology. New York: Springer.
209. Plummer, M. and D. G. Clayton (1996). Estimation of population exposure in ecological studies. Journal of the Royal Statistical Society B 58,113-126.
210. Propp, J. and D. Wilson (1996). Exact sampling with coupled markov chains. Random Structures and Algortithms 9,223-252.
211. Rao, J. K. N. (2003). Small Area Estimation.New York: Wiley.
212. Richardson, S., A. Thomson, N. Best, and P. Elliott (2004). Interpreting posterior relative risk estimates in disease mapping studies. Environmental Health Perspectives 112,1016-1025.
213. Ripley, B. D. (1981). Spatial Statistics.New York: Wiley.
214. Ripley, B. D. (1987). Stochastic Simulation.New York: Wiley.
215. Ripley, B. D. (1988). Statistical Inference for Spatial Processes.New York: Cambridge University Press.
216. Robert, C. and G. Casella (2005). Monte Carlo Statistical Methods (2nd ed.). New York: Springer.
217. Royle, A. and M. Berliner (1999). A hierarchical approach to multivariate spatial modeling and prediction. Journal of Agricultural, Biological and Environmental Statistics 4, 29-56.
218. Rue, H. and L. Held (2005). Gaussian Markov Random Fields: Theory and Applications.New York: Chapman &Hall/CRC.
219. Salway, R. and J. Wakefield (2005). Sources of bias in ecological studies of non-rare events. Environmental and Ecological Statistics 12,321-347.
220. Schlattman, P. and D. Bohning (1993). Mixture models and disease mapping. Statistics in Medicine 12,1943-1950.
221. Smith, A. F. M. and A. E. Gelfand (1992). Bayesian statistics without tears: A sampling-resampling perspective. American Statistician 46,84-88.
222. Smith, A. F. M. and G. Roberts (1993). Bayesian computation via the Gibbs Sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society B 55, 3-23.
223. Snow, J. (1854). On the Mode of Communication of Cholera (2nd ed.). London: Churchill Livingstone.
224. Spiegelhalter, D., A. Thomas, N. Best, and D. Lunn (2007). WinBUGS manual. Cambridge, UK: MRC Biostatistics Unit. version 1.4.3.
225. Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van der Linde (2002). Bayesian deviance, the effective number of parameters and the comparison of arbitrarily complex models. Journal of the Royal Statistical Society B 64, 583-640.
226. Spiegelhalter, D. J., N. G. Best, W. R. Gilks, and H. Inskip (1996). Hepatitis B: a case study in MCMC methods. In W. R. Gilks, S. Richardson, and D. J. Spiegelhalter (Eds.), Markov Chain Monte Carlo in Practice.London: Chapman &Hall.
227. Steele, F., H. Goldstein, and W. Browne (2004). A general multilevel multistate competing risks model for event history data, with an application to a study of contraceptive use dynamics. Statistical Modelling 4,145-159.
228. Stern, H. and Y. Jeon (2004). Applying structural equation models with incomplete data. In A. Gelman and X.-L. Meng (Eds.), Applied Bayesain Modeling and Causal Inference from Incomplete-Data Perspectives, Chapter 30, 331-342. London: Wiley.
229. Stern, H. S. and N. A. C. Cressie (1999). Inference for extremes in disease mapping. In A. B. Lawson, A. Biggeri, D. Boehning, E. Lesaffre, J. F. Viel, and R. Bertollini (Eds.), Disease Mapping and Risk Assessment for Public Health, Chapter 5. New York: Wiley.
230. Stern, H. S. and N. A. C. Cressie (2000). Posterior predictive model checks for disease mapping models. Statistics in Medicine 19,2377-2397.
231. Stevenson, M., R. Morris, A. B. Lawson, J. Wilesmith, J. M. Ryan, and R. Jackson (2005). Area level risks for BSE in British cattle before and after the July 1988 meat and bone meal feed ban. Preventive Veterinary Medicine 69,129-144.
232. Stevenson, M. A., R. S. Morris, A. B. Lawson, J. W. Wilesmith, J. B. M. Ryan, and R. A. Jackson (2001). Spatial regression analysis of the bovine spongiform encephalopathy epidemic in Great Britain. In A. C. Gatrell and P. Durr (Eds.), The GISVET conference- Application of Geographical Information Systems and Spatil Analysis to Veterinary Science, Lancaster.
233. Stevenson, M. A., J. W. Wilesmith, J. B. M. Ryan, R. S. Morris, A. B. Lawson, D. U. Pfeiffer, and D. Lin (2000). Descriptive spatial analysis of the epidemic of bovine spongiform encephalopathy in Great Britain to June 1997. Veterinary Record 147,379-384.
234. Sturtz, S., U. Ligges, and A. Gelman (2005). R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software 12,1-16.
235. Sun, D., R. Tsutakawa, H. Kim, and Z. He (2000). Spatio-temporal interaction with disease mapping. Statistics in Medicine 19,2015-2035.
236. Sutton, S. (2005). The modeling of spatially-referenced recurrent event data in a South Carolina Population.PhD thesis, University of South Carolina.
237. Tanner, M. A. (1996). Tools for Statistical Inference (3rd ed.). New York: Springer Verlag.
238. Thomas, A., N. Best, D. Lunn, R. Arnold, and D. Spiegelhalter (2004). Geobugs user manual v 1.2. www.mrc-bsu.cam.ac.uk/bugs.
239. Tibshirani, R. and T. Hastie (1987). Local likelihood estimation. Journal of the American Statistical Association 82, 559-568.
240. Tierney, L. and J. Kadane (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association 81, 82-86.
241. van Lieshout, M. and A. Baddeley (2002). Extrapolating and interpolating spatial patterns. In A. B. Lawson and D. Denison (Eds.), Spatial Cluster-Modelling, Chapter 4, 61-86. New York: CRC Press.
242. Varin, C., G. Høst, and Ö. Skare (2005). Pairwise likelihood infernce in spatial generalized linear mixed models. Computational Statistics and Data Analysis 49,1173-1191.
243. Verbeke, G. and G. Molenberghs (2000). Linear Mixed Models for Longitudinal Data.New York: Springer.
244. Vidal-Rodiero, C. L. and A. B. Lawson (2005). An evaluation of edge effects in disease mapping. Computational Statistics and Data Analysis 49,45-62.
245. Voss, D. (2004). Using ecological inference for contextual research. In G. King, O. Rosen, and M. Tanner (Eds.), Ecological Inferencee: New Methodological Strategies, Chapter 3. New York: Cambridge University Press.
246. Wackernagel, H. (2003). Multivariate Geostatistics (3rd ed.). New York: Springer.
247. Wakefield, J. and G. Shaddick (2006). Health-exposure modeling and the ecological fallacy. Biostatistics 7,438-455.
248. Wakefield, J. C. and S. Morris (2001). The Bayesian modeling of disease risk in relation to a point source. Journal of the American Statistical Association 96,77-91.
249. Waller, L. and C. Gotway (2004). Applied Spatial Statistics for Public Health Data. New York: Wiley.
250. Waller, L. A., B. P. Carlin, H. Xia, and A. E. Gelfand (1997). Hierarchical spatio-temporal mapping of disease rates. Journal of the American Statistical Association 92,607-617.
251. Wang, F. and M. Wall (2003). Generalized common spatial factor model. Biostatitsics 4, 569-582.
252. Wikle, C. K. (2002). Spatial modelling of count data: A case study in modelling breeding bird survey data on large spatial domains. In A. B. Lawson and D. G. T. Denison (Eds.), Spatial Cluster Modelling, Chapter 11. London: CRC Press.
253. Williams, F., A. Lawson, and O. Lloyd (1992). Low sex ratios of births in areas at risk from air pollution from incinerators, as shown by geographical analysis and 3-dimensional mapping. International Journal of Epidemiology 21, 311-319.
254. Wolpert, R. L. and K. Ickstadt (1998). Poisson/gamma random field models for spatial statistics. Biometrika 85,251-267.
255. Xia, H. and B. P. Carlin (1998). Spatio-temporal models with errors in co-variates: Mapping Ohio lung cancer mortality. Statistics in Medicine 17, 2025-2043.
256. Yu, B. and P. Mykland (1998). Looking at Markov samplers through cusum path plots: A simple diagnostic idea. Statistics and Computing 8,275-286.
257. Zhang, S., D. Sun, C. He, and M. Schootman (2006). A Bayesian semi-parametric model for colorectal cancer incidences. Statistics in Medicine 25, 285-309.
258. Zhou, H., A. B. Lawson, J. Hebert, E. Slate, and E. Hill (2007). A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer. Statistics in Medicine 27(9). DOI 10.1002/sim:3024.
259. Zhou, H., A. B. Lawson, J. Hebert, E. Slate, and E. Hill (2008). Joint spatial survival modelling for the date of diagnosis and the vital outcome for prostate cancer. Statistics in Medicine. to appear.
260. Zhu, L., B. Carlin, and A. Gelfand (2003). Hierarchical regression with misaligned spatial data: relating ambient ozone and pediatric asthma er visits in Atlanta. Environmetrics 14,537-557.
261. Zhu, L. and B. P. Carlin (2000). Comparing hierarchical models for spatio-temporally misaligned data using the DIC criterion. Statistics in Medicine 19,2265-2278.
262. Zia, H., B. P. Carlin, and L. A. Waller (1997). Hierarchical models for mapping Ohio lung cancer rates. Environmetrics 8,107-120.