Disease Mapping with WinBUGS and MLwiN

Disease Mapping with WinBUGS and MLwiN

Volumn , Issue , 2004, Pages 1-279

  Lawson, Andrew B ^a   Browne, William J ^b   Vidal Rodeiro, Carmen L ^a  

^a UNIVERSITY OF SOUTH CAROLINA   (United States)

^b UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ECOLOGY; MAPPING; PUBLIC HEALTH; RISK PERCEPTION; STUDENTS;

DISEASE INCIDENCE; DISEASE MAPPING; ECOLOGICAL ANALYSIS; ECOLOGICAL STUDIES; GRADUATE STUDENTS; MULTILEVEL METHOD; PRACTICAL INTRODUCTION; STATISTICAL KNOWLEDGE;

RISK ASSESSMENT;

EID: 84982233894     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/0470856068     Document Type: Book

References (127)

1. Aitkin, M. (1996a). Empirical Bayes shrinkage using posterior random effect means from nonparametric maximum likelihood estimation in general random effect models. In A. Forcina, G. M. Marchetti, R. Hatzinger, and G. Galmacci (Eds), Proceedings of the 11th International Workshop on Statistical Modelling, pp. 87-92. Graphos, Citta di Castello.
2. Aitkin, M. (1996b). A general maximum likelihood analysis of overdispersion in generalised linear models. Statistics and Computing 6, 251-262.
3. Aitkin, M. and N. Longford (1986). Statistical modelling in school effectiveness studies (with discussion). Journal of the Royal Statistical Society 149, 1-43.
4. Aitkin, M., D. Anderson, and J. Hinde (1981). Statistical modelling of data on teaching styles (with discussion). Journal of the Royal Statistical Society 144, 148-161.
5. Assuncao, R. (2003). Space varying coeffcient models for small area data. Environmetrics. To appear.
6. Assuncao, R., J. Potter, and S. Cavenghi (2002). A Bayesian space varying parameter model applied to estimating fertility schedules. Statistics in Medicine 21, 2057-2075.
7. Banerjee, S. and B. P. Carlin (2003). Semiparametric spatio-temporal frailty modeling. Environmetrics. To appear.
8. 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.
9. 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.
10. Bernardinelli, L., D. G. Clayton, C. Montomoli (1995b) Bayesian estimates of disease maps: how important are priors? Statistics in Medicine 14, 2411-2431.
11. Bernardinelli, L., C. Pascutto, C. Montomoli, J. Komakec, and W. Gilks (1999). Ecological regression with errors in covariates. In A. B. Lawson, D. Boehning, E. Lasaffree, A. Biggeri, J. F. Viel, and R. Bertolline (Eds), Disease Mapping and Risk Assessment for Public Health. Chichester: John Wiley & Sons, Ltd.
12. Bernardinelli, L., C. Pascutto, C. Montomoli, and W. Gilks (2000). Investigating the genetic association between diabetes and malaria: an application of Bayesian ecological regression models with errors in covariates. In P. Elliott, J. Wakefield, N. G. Best, and D. Briggs (Eds), Spatial Epidemiology. Methods and Applications. Oxford: Oxford University Press.
13. Bernardo, J. M. and A. F. M. Smith (1994). Bayesian Theory. New York: John Wiley & Sons, Inc.
14. Berzuini, C., N. G. Best, W. R. Gilks, and C. Larissa (1997). Dynamic conditional independence models and Markov Chain Monte Carlo methods. Journal of the American Statistical association 92, 1403-1412.
15. Besag, J. and P. J. Green (1993). Spatial statistics and Bayesian computation. Journal of the Royal Statistical Society, Series B 55, 25-37.
16. 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.
17. 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 162, 363-382.
18. Best, N. G., M. K. Cowles, and K. Vines (1995). CODA: Convergence Diagnosis and Output Analysis Software for Gibbs Sampling Output. Cambridge, England: MRC Biostatistics Unit.
19. Biggeri, A., F. Divino, A. Frigessi, A. B. Lawson, D. Boehning, E. Lesaffree, and J. F. Viel (1999). Introduction to spatial models in ecological analyses. In A. B. Lawson, D. Boehning, E. Lasaffree, A. Biggeri, J. F. Viel, and R. Bertolline (Eds), Disease Mapping and Risk Assessment for Public Health. Chichester: John Wiley & Sons, Ltd.
20. Bock, R. D. and M. Aitkin (1981). Marginal maximum likelihood estimation of item parameters: an application of an EM algorithm. Psychometrika 46, 443-459.
21. Boehning, D., E. Dietz, and P. Schlattmann (2000). Space-time mixture modelling of public health data. Statistics in Medicine 19, 2333-2344.
22. Bowman, A. and A. Azzalini (1997). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. London: Oxford University Press.
23. Breslow, N. and D. G. Clayton (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9-25.
24. Breslow, N. and N. Day (1987). Statistical Methods in Cancer Research, Vol. 2: The Design and Analysis of Cohort Studies. Lyon: International Agency for Research on Cancer.
25. Brooks, S. and A. E. Gelman (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434-455.
26. Browne, W. (1998). Applying MCMC methods to multilevel models. PhD thesis, University of Bath.
27. Browne, W. (2003). MCMC Estimation in MLwiN. London: Institute of Education.
28. Browne, W. and D. Draper (2000). Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models. Computational Statistics 15, 391-420.
29. Browne, W., H. Goldstein, and J. Rasbash (2001). Multiple membership multiple classification (MMMC) models. Statistical Modelling 1, 103-124.
30. Brunsdon, C., A. E. Fotheringham, and M. Charlton (1999). Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science 39, 497-524.
31. Bryk, A. and S. Raudenbush (1992). Hierarchical Linear Models. Newbury Park: Sage.
32. Bryk, A., S. Raudenbush, M. Seltzer, and R. Congdon (1988). An Introduction to HLM: Computer Program and User's Guide (2nd edn). Chicago: University of Chicago, Department of Education.
33. Carlin, B. P. and S. Banerjee (2003). Hierarchical multivariate CAR models for spatiotemporally correlated survival data. In J. M. Bernardo et al. (Eds), Bayesian Statistics 7. Oxford: Oxford University Press.
34. Carlin, B. P. and T. A. Louis (1996). Bayes and Empirical Bayes Methods for Data Analysis. London: Chapman & Hall.
35. Carstairs, V. (1981). Small area analysis and health service research. Community Medicine 3, 131-139.
36. Casella, G. and E. I. George (1992). Explaining the Gibbs Sampler. The American Statistician 46, 167-174.
37. Chen, M., Q. Shao, and J. Ibrahim (2000). Monte Carlo Methods in Bayesian Computation. New York: Springer Verlag.
38. Clayton, D. G. (1991). A Monte Carlo method for Bayesian inference in frailty models. Biometrics 47, 467-485.
39. 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.
40. Clayton, D. G. and J. Kaldor (1987). Empirical Bayes estimates of age-standardised relative risks for use in disease mapping. Biometrics 43, 671-691.
41. Clayton, D. G., L. Bernardinelli, and C. Montomoli (1993). Spatial correlation in ecological analysis. International Journal of Epidemiology 22, 1193-1202.
42. Cliff, A. D. and J. K. Ord (1981). Spatial Processes: Models and Applications. London: Pion.
43. Congdon, P. (2003). Applied Bayesian Modelling. Clinchester: John Wiley & Sons, Ltd.
44. Cressie, N. A. C. (1993). Statistics for Spatial Data (revised edn). New York: John Wiley & Sons, Inc.
45. Cuzick, J. and M. Hills (1991). Clustering and clusters-summary. In G. Draper (Ed.), Geographical Epidemiology of Childhood Leukaemia and Non-Hodgkin Lymphomas in Great Britain 1966-1983, pp. 123-125. London: HMSO.
46. Daley, D. and D. Vere-Jones (1988). An Introduction to the Theory of Point Processes. New York: Springer Verlag.
47. Devine, O. and T. Louis (1994). A constrained empirical Bayes estimator for incidence rates in areas with small populations. Statistics in Medicine 13, 1119-1133.
48. Diggle, P. J., J. Tawn, and R. Moyeed (1998). Model-based geostatistics. Journal of the Royal Statistical Society C 47, 299-350.
49. Diggle, P. J., S. Morris, and J. C. Wakefield (2000). Point-source modelling using matched case-control data. Biostatistics 1, 1-17.
50. Doucet, A., N. de Freitas, and N. Gordon (Eds) (2001). Sequential Monte Carlo Methods in Practice. New York: Springer Verlag.
51. Elliott, P., J. C. Wakefield, N. G. Best, and D. J. Briggs (Eds) (2000). Spatial Epidemiology: Methods and Applications. London: Oxford University Press.
52. 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.
53. Ferreira, J., D. Denison, and C. Holmes (2002). Partition modelling. In A. B. Lawson and D. Denison (Eds), Spatial Cluster Modelling, Chapter 7, pp. 125-145. New York: CRC Press.
54. Fotheringham, A. E., M. Charlton, and C. Brunsdon (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30, 1905-1927.
55. Gelman, A. E. and D. Rubin (1992). Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7, 457-511.
56. Gelman, A., J. Carlin, H. Stern, and D. Rubin (1995). Bayesian Data Analysis. London: Chapman & Hall.
57. Gilks, W. and P. Wild (1992). Adaptive rejection sampling for Gibbs Sampling. Applied Statistics 41, 337-348.
58. 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.
59. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter (Eds) (1996). Markov Chain Monte Carlo in Practice. London: Chapman & Hall.
60. Glick, B. J. (1979). The spatial autocorrelation of cancer mortality. Social Science and Medicine. 13D, 123-130.
61. Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika 73, 43-56.
62. Goldstein, H. (1989). Restricted unbiased iterative generalised least squares estimation. Biometrika 76, 622-623.
63. Goldstein, H. (1991). Nonlinear multilevel models with an application to binary response data. Biometrika 78, 45-51.
64. Goldstein, H. (1995). Multilevel Statistical Models (2nd edn). London: Edward Arnold.
65. Goldstein, H. and J. Rasbash (1996). Improved approximations for multilevel models with binary response. Journal of the Royal Statistical Society 159, 505-513.
66. Hedeker, D. and R. Gibbons (1994). A random-effects ordinal regression model for multilevel analysis. Biometrics 50, 933-944.
67. Hill, P. and H. Goldstein (1998). Multilevel modelling of educational data with crossclassifications and missing identification of units. Journal of Educational and Behavioral Statistics 23, 117-128.
68. Kelsall, J. and P. Diggle (1998). Spatial variation in risk of disease: a nonparametric binary regression approach. Applied Statistics 47, 559-573.
69. Knorr-Held, L. (2000). Bayesian modelling of inseparable space-time variation in disease risk. Statistics in Medicine 19, 2555-2567.
70. Knorr-Held, L. and J. Besag (1998). Modelling risk from a disease in time and space. Statistics in Medicine 17, 2045-2060.
71. 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.
72. Knorr-Held, L. and G. Rasser (2000). Bayesian detection of clusters and discontinuities in disease maps. Biometrics 56, 13-21.
73. Langford, I., G. Bentham, and A. McDonald (1998). Multilevel modelling of geographically aggregated health data: a case study on malignant melanoma mortality and uv exposure in the European Community. Statistics in Medicine 17, 41-58.
74. Langford, I., A. Leyland, J. Rasbash, and H. Goldstein (1999). Multilevel modelling of the geographical distribution of rare diseases. Journal of the Royal Statistical Society 48, 253-268.
75. Lawson, A. B. (1993). On the analysis of mortality events around a prespecified fixed point. Journal of the Royal Statistical Society A 156, 363-377.
76. Lawson, A. B. (1994). On using spatial Gaussian priors to model heterogeneity in environmental epidemiology. The Statistician 43, 69-76. Proceedings of the Practical Bayesian Statistics Conference.
77. Lawson, A. B. (1997). Some spatial statistical tools for pattern recognition. In A. Stein, F. W. T. P. de Vries, and J. Schut (Eds), Quantitative Approaches in Systems Analysis, Vol. 7, pp. 43-58. C. T. de Wit Graduate School for Production Ecology, Wageningen, Netherlands.
78. Lawson, A. B. (2001). Statistical Methods in Spatial Epidemiology. Chichester: John Wiley & Sons, Ltd.
79. Lawson, A. B. and A. Clark (2002). Spatial mixture relative risk models applied to disease mapping. Statistics in Medicine 21, 359-370.
80. 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.
81. Lawson, A. B., A. Biggeri, and C. Lagazio (1996). Modelling heterogeneity in discrete spatial data models via MAP and MCMC methods. In A. Forcina, G. Marchetti, R. Hatzinger, and G. Galmacci (Eds), Proceedings of the 11th International Workshop on Statistical Modelling, pp. 240-250. Graphos, Citta di Castello.
82. 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: John Wiley & Sons, Ltd.
83. 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.
84. Lesaffre, E. and B. Spiessens (2001). On the effect of the number of quadrature points in a logistic random-effects model: an example. Journal of the Royal Statistical Society C, 50, 325-336.
85. Leyland, A. and H. Goldstein (Eds) (2001). Multilevel Modelling of Health Statistics. Chichester: John Wiley and Sons, Ltd.
86. Leyland, A. and A. McLeod (2000). Mortality in England and Wales, 1979-1992: An introduction to multilevel modelling using MLwiN. Occasional Paper no. 1, MRC Social and Public Health Sciences Unit.
87. Leyland, A., I. Langford, J. Rashbash, and H. Goldstein (2000). Multivariate spatial models for event data. Statistics in Medicine 19, 2469-2478.
88. Longford, N. (1987). A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometrika 74, 817-827.
89. Longford, N. (1988). VARCL - software for variance components analysis of data with hierarchically nested random effects (maximum likelihood). Princeton, NJ: Educational Testing Service.
90. MacEachren, A. M. (1995). How Maps Work: Representation, Visualization and Design. New York: Guilford Press.
91. MacNab, Y. C. and C. B. Dean (2002). Spatio-temporal modelling of rates for the construction of disease maps. Statistics in Medicine 21, 347-358.
92. Manton, K., M. Woodbury, and E. Stallard (1981). A variance components approach to categorical data models with heterogeneous mortality rates in North Carolina counties. Biometrics 37, 259-269.
93. Marshall, R. (1991). Mapping disease and mortality rates using empirical Bayes estimators. Applied Statistics 40, 283-294.
94. Mollie, A. (2000). Bayesian mapping of Hodgkin's disease in France. In P. Elliott, J. Wakefield, N. G. Best, and D. Briggs (Eds), Spatial Epidemiology. Methods and Applications. Oxford: Oxford University Press.
95. Monmonier, M. (1996). How to Lie with Maps (2nd edn). London: University of Chicago Press.
96. Neal, R. (1998). Suppressing random walks in Markov chain Monte Carlo using ordered over-relaxation. In M. I. Jordan (Ed.), Learning in Graphical Models. Dordrecht: Kluwer Academic Publishers.
97. Neal, R. M. (2003). Slice sampling. Annals of Statistics 31, 1-34.
98. Panopsky, M. A. and J. A. Dutton (1984). Atmospheric Turbulence. New York: John Wiley & Sons, Inc.
99. Pickle, L. W. and D. J. Hermann (1995). Cognitive aspects of statistical mapping. Technical Report 18, NCHS O.ce of Research and Methodology, Washington, DC.
100. Pickle, L., M. Mungiole, G. Jones, and A. White (1999). Exploring spatial patterns of mortality: the new atlas of United States mortality. Statistics in Medicine 18, 3211-3220.
101. Rabe-Hesketh, S., T. Toulopoulou, and R. Murray (2001). Multilevel modeling of cognitive function in schizophrenic patients and their first degree relatives. Multivariate Behavioural Research 36, 279-298.
102. Rasbash, J. and H. Goldstein (1994). Efficient analysis of mixed hierarchical and crossed random structures using a multilevel model. Journal of Behavioral Statistics 19, 337-350.
103. Rasbash, J., W. Browne, H. Goldstein, M. Yang, I. Plewis, M. Healy, G. Woodhouse, D. Draper, I. Langford, and T. Lewis (2000). A User's Guide to MLwiN. London: Institute of Education.
104. Raudenbush, S., M. Yang, and M. Yosef (2000). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. Journal of Computational and Graphical Statistics 9, 141-157.
105. Richardson, S. (2003). Spatial models in epidemiological applications. In P. Green, N. Hjort, and S. Richardson (Eds), Highly Structured Stochastic Systems. London: Oxford University Press.
106. Richardson, S., C. Monfort, M. Green, G. Draper, and C. Muirhead (1995). Spatial variation of natural radiation and childhood leukaemia incidence in Great Britain. Statistics in Medicine 14, 2487-2501.
107. Ripley, B. D. (1987). Stochastic Simulation. New York: John Wiley & Sons, Inc.
108. Robert, C. P. and G. Casella (1999). Monte Carlo Statistical Methods. New York: Springer Verlag.
109. Schlattman, P. and D. Boehning (1993). Mixture models and disease mapping. Statistics in Medicine 12, 1943-1950.
110. Smith, A. F. M. and A. E. Gelfand (1992). Bayesian statistics without tears: a samplingresampling perspective. American Statistician 46, 84-88.
111. 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.
112. Smith, B. (1999). BOA, BUGS Output Analysis Program. The University of Iowa College of Public Health.
113. Snow, J. (1854). On the Mode of Communication of Cholera (2nd edn). London: Churchill Livingstone.
114. 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.
115. Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van der Linde (2002a). Bayesian deviance, the effective number of parameters and the comparison of arbitrarily complex models. Journal of the Royal Statistical Society B, 64, 583-640.
116. Spiegelhalter, D. J., A. Thomas, N. G. Best, and D. Lunn (2002b). WinBugs User Manual. Version 1.4. Cambridge, England: MRC Biostatistics Unit.
117. Stern, H. S. and N. A. Cressie (1999). Inference for extremes in disease mapping. In A. B. Lawson, D. Bohning, E. Lasaffree, A. Biggeri, J. F. Viel, and R. Bertolline (Eds), Disease Mapping and Risk Assessment for Public Health. Chichester: John Wiley & Sons, Ltd.
118. Stern, H. S. and N. A. C. Cressie (2000). Posterior predictive model checks for disease mapping models. Statistics in Medicine 19, 2377-2397.
119. Tanner, M. A. (1996). Tools for Statistical Inference (3rd edn). New York: Springer Verlag.
120. Thomas, A., N. G. Best, R. Arnold, and D. J. Spiegelhalter (2002). GeoBUGS User Manual. Version 1.1. London: Imperial College School of Medicine.
121. Tsutakawa, R. (1988). Mixed model for analysing geographic variability in mortality rates. Journal of the American Statistical Association 83, 37-42.
122. 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.
123. Waller, L. A., B. P. Carlin, H. Xia, and A. E. Gelfand (1997). Hierarchical spatiotemporal mapping of disease rates. Journal of the American Statistical Association 92, 607-617.
124. Walter, S. D. (1993). Visual and statistical assessment of spatial clustering in mapped data. Statistics in Medicine 12, 1275-1291.
125. Yang, M., J. Rasbash, H. Goldstein, and M. Barbosa (2000). MLwiN Macros for Advanced Multilevel Modelling. London: Institute of Education.
126. Yu, B. and P. Mykland (1998). Looking at Markov samplers through cusum path plots: A simple diagnostic idea. Statistics and Computing 8, 275-286.
127. Zia, H., B. P. Carlin, and L. A. Waller (1997). Hierarchical models for mapping Ohio lung cancer rates. Environmetrics 8, 107-120.