Spatial variability in relative survival from female breast cancer

Journal of the Royal Statistical Society. Series A: Statistics in Society

Volumn 175, Issue 1, 2012, Pages 107-134

  Saez, Marc ^a   Barceló, Maria Antònia ^a   Martos, Carmen ^b   Saurina, Carme ^a   Marcos Gragera, Rafael ^a,d   Renart, Gemma ^a   Ocaña Riola, Ricardo ^e   Feja, Cristina ^c   Alcalá, Tomás ^c,f  

^a UNIVERSITY OF GIRONA   (Spain)

^b Center for Public Health Research   (Spain)

^c Aragon Institute of Health Care   (Spain)

^d CATALAN INSTITUTE OF ONCOLOGY   (Spain)

^e ANDALUSIAN SCHOOL OF PUBLIC HEALTH   (Spain)

^f UNIVERSITY OF ZARAGOZA   (Spain)

Author keywords

Bayesian hierarchical models; Breast cancer; Geographical variations; Relative survival; Reliability

Indexed keywords

EID: 84855917625     PISSN: 09641998     EISSN: 1467985X     Source Type: Journal    
DOI: 10.1111/j.1467-985X.2011.00720.x     Document Type: Article

References (91)

1. Allemani, C., Sant, M., De Angelis, R., Marcos-Gragera, R., Coebergh, J. W. and the EUROCARE Working Group (2006) Hodgkin disease survival in Europe and the U.S.: prognostic significance of morphologic groups. Cancer, 107, 352-360.
2. Andersen, P. K., Borgan, Ø., Gill, R. D. and Keiding, N. (1995) Statistical Models based on Counting Processes. Berlin: Springer.
3. Banerjee, S. and Carlin, B. P. (2003) Semiparametric spatio-temporal frailty modelling. Environmetrics, 14, 523-535.
4. Banerjee, S., Wall, M. M. and Carlin, B. P. (2003) Frailty modelling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics, 4, 123-142.
5. Barceló, M. A., Saez, M., Cano-Serral, G., Martínez-Beneito, M. A., Martínez, J. M., Borrell, C., Ocaña-Riola, R., Montoya, I., Calvo, M., López-Abente, G., Rodríguez-Sanz, M., Toro, S., Alcalá, J. T., Saurina, C., Sánchez-Villegas, P. and Figueiras, A. (2008) Methods to smooth mortality indicators: application to the an-alysis of inequalities in mortality in Spanish cities (the MEDEA Project) (in Spanish). Gac. Sanit., 22, 596-608.
6. Berrino, F., De Angelis, R., Sant, M., Rosso, S., Bielska-Lasota, M., Coebergh, J. W., Santaquilani, M. and the EUROCARE Working Group (2007) Survival for eight major cancers and all cancers combined for European adults diagnosed in 1995-99: results of the EUROCARE-4 study. Lancet Oncol., 8, 752-753, 773-783, 868, 1050-1054.
7. Besag, J. (1974) Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Statist. Soc. B, 36, 192-236.
8. Besag, J., York, J. and Mollié, A (1991) Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Statist. Math., 43, 1-59.
9. Best, N. (2006) Bayesian approaches to cancer survival analysis. Short Course on 'Cancer survival: principles, methods and applications ', London, Apr. 3rd-7th.
10. Brenner, H. and Hakulinen, T. (2004) Are patients diagnosed with breast cancer before age 50 years ever cured? J. Clin. Oncol., 22, 392-394, 432-438.
11. Breslow, N. (1972) Discussion on 'Regression models and life-tables' (by D. R. Cox). J. R. Statist. Soc. B, 34, 216-217.
12. Breslow, N. E. and Day, N. E. (1987) Statistical Methods in Cancer Research, vol. II, The Design and Analysis of Cohort Studies. Lyon: International Agency for Research on Cancer.
13. Brooks, S. P. and Gelman, A. (1998) Alternative methods for monitoring convergence of iterative simulations. J. Computnl Graph. Statist., 7, 434-455.
14. Carlin, B. P., Gelman, A. and Neal, R. M. (1998) Markov Chain Monte Carlo in practice: a roundtable discussion. Am. Statistn, 52, 93-100.
15. Carlsen, K., Høybye, M. T., Oksbjerg-Dalton, S. and Tjønneland, A. (2008) Social inequality and incidence of and survival from breast cancer in a population-based study in Denmark, 1994-2003. Eur. J. Cancer, 44, 1996-2002.
16. Celeux, G., Forbes, F., Robert, C. P. and Titterington, D. M. (2006) Deviance Information Criteria for missing data models. Baysn Anal., 1, 651-674.
17. Chia, K. S., Du, W. B., Sankaranarayanan, R., Sankila, R., Wang, H., Lee, J., Seow, A. and Lee, H. P. (2004) Do younger female breast cancer patients have a poorer prognosis?: results from a population-based survival analysis. Int. J. Cancer, 108, 761-765.
18. Clayton, D. G., Bernadinelli, L. and Montomoli, C. (1993) Spatial correlation in ecological analysis. Int. J. Epidem., 22, 1193-1202.
19. Clegg, L. X., Reichman, M. E., Miller, B. A., Hankey, B. F., Singh, G. K., Lin, Y. D., Goodman, M. T., Lynch, C. F., Schwartz, S. M., Chen, V. W., Bernstein, L., Gomez, S. L., Graff, J. J., Lin, C. C., Johnson, N. J. and Edwards, B. K. (2009) Impact of socioeconomic status on cancer incidence and stage at diagnosis: selected findings from the surveillance, epidemiology, and end results National Longitudinal Mortality Study. Cancer Causes Contr., 20, 417-435.
20. Collett, D. (1994) Modelling Survival Data in Medical Research. London: Chapman and Hall.
21. Cox, D. R. (1972) Regression models and life-tables (with discussion). J. R. Statist. Soc. B, 34, 187-220.
22. Curado, M. P., Edwards, B., Shin, H. R., Storm, H., Ferlay, J., Heanue, M. and Boyle, P. (eds) (2007) Cancer Incidence in Five Continents, vol. IX. Lyon: International Agency for Research on Cancer.
23. Department of Health (2006) Tackling Hospital Waiting: the 18-week Patient Pathway: an Implementation Framework. London: Stationery Office.
24. Dickman, P. W., Auvinen, A., Voutilainen, E. T. and Hakulinen, T. (1999) Measuring social class differences in cancer patient survival: is it necessary to control for social class differences in general population mortality? A Finnish population-based study. J. Epidem. Commty Hlth, 52, 727-734.
25. Dickman, P. W., Gibberd, R. W. and Hakulinen, T. (1997) Estimating potential savings in cancer deaths by eliminating regional and social class variation in cancer survival in the Nordic countries. J. Epidem. Commty Hlth, 51, 289-298.
26. Dickman, P. W. and Hakulinen, T. (2008) Population-based Cancer Survival Analysis. New York: Wiley.
27. Dickman, P. W., Slogget, A., Hills, M. and Hakulinen, T. (2004) Regression models for relative survival. Statist. Med., 23, 51-64.
28. Domínguez-Berjón, F., Borrell, C., Cano-Serral, G., Esnaola, S., Nolasco, A., Pasarín, M. I., Ramis, R., Saurina, C. and Escolar-Pujolar, A. (2008) Constructing a deprivation index based on census data in large Spanish cities (the MEDEA Project) (in Spanish). Gac. Sanit., 22, 179-187.
29. Eaker, S., Halmin, M., Bellocco, R., Bergkvist, L., Ahlgren, J., Holmberg, L., Lambe, M. and the Uppsala/Orebro Breast Cancer Group (2009) Social differences in breast cancer survival in relation to patient management within a National Health Care System (Sweden). Int. J. Cancer, 124, 180-187.
30. Efron, B. and Morris, C. (1975) Data analysis using Stein's estimator and its generalizations. J. Am. Statist. Ass., 70, 311-319.
31. Elliott, P. and Wartenberg, D. (2004) Spatial epidemiology: current approaches and future challenges. Environ. Hlth Perspect., 112, 998-1006.
32. Ellis, L., Rachet, B. and Coleman, M. P. (2007) Cancer survival indicators by Cancer Network: a methodological perspective. Hlth Statist. Q., 36, 36-41.
33. Ellis, L., Shah, A., Walters, S., Coleman, M. P., Cooper, N. and Westlake, S. (2009) Trends in cancer survival in Spearhead Primary Care Trusts in England, 1998-2004. Hlth Statist. Q., 41, 7-12.
34. Engeland, A., Haldorsen, T., Dickman, P. W., Hakulinen, T., Möller, T. R., Storm, H. H. and Tulinius, H. (1998) Relative survival of cancer patients: a comparison between Denmark and the other Nordic Countries. Acta Oncol., 37, 49-59.
35. Engeland, A., Haldorsen, T., Tretli, S., Hakulinen, T., Hörte, L. G., Luostarinen, T., Schou, G., Sigvaldason, H., Storm, H. H. and Tulinius, H. (1995) Prediction of cancer mortality in the Nordic countries up to the years 2000 and 2010, on the basis of relative survival analysis: a collaborative study of the five Nordic Cancer Registries. Acta Path. Microbiol. Immunol. Scand., suppl., 49, 1-161.
36. ESRI (2009) Arc GIS 9.1. Redlands: ESRI. (Available from).
37. Estève, J., Benhamou, E., Croasdale, M. and Raymond, L. (1990) Relative survival and the estimation of net survival: elements for further discussion. Statist. Med., 9, 529-538.
38. von Euler-Chelpin, M., Olsen, A. H., Njor, S., Vejborg, I., Schwartz, E. and Lynge, E. (2008) Socio-demographic determinants of participation in mammography screening. Int. J. Cancer, 122, 418-423.
39. EUROCARE (2009)European cancer registry-based study on survival and care of cancer patients. EUROCARE.(Available from).
40. EUROSTAT (2009)European cancer registry-based study on survival and care of cancer patients. EUROCARE.(Available from).
41. Farrow, D. C., Samet, J. M. and Hunt, W. C. (1996) Regional variation in survival following the diagnosis of cancer. J. Clin. Epidem., 49, 843-847.
42. Gelfand, A. E. and Smith, A. F. M. (1990) Sampling-based approaches to calculating marginal densities. J. Am. Statist. Ass., 85, 398-409.
43. Gelman, A. (2005) Prior distributions for variance parameters in hierarchical models. Baysn Anal., 1, 1-19.
44. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2004) Bayesian Data Analysis. London: Chapman and Hall-CRC.
45. Gentil-Brevet, J., Colonna, M., Danzon, A., Grosclaude, P., Chaplain, G., Velten, M., Bonnetain, F. and Arveux, P. (2008) The influence of socio-economic and surveillance characteristics on breast cancer survival: a French population-based study. Br. J. Cancer, 98, 217-224.
46. Gibberd, R., Pathmeswaran, A. and Burtenshaw, K. (2000) Using clinical indicators to identify areas for quality improvement. J. Qual. Clin. Pract., 20, 136-144.
47. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds) (1996) Markov Chain Monte Carlo in Practice. New York: Chapman and Hall.
48. Gnerlich, J. L., Deshpande, A. D., Jeffe, D. B., Sweet, A., White, N. and Margenthaler, J. A. (2009) Elevated breast cancer mortality in women younger than age 40 years compared with older women is attributed to poorer survival in early-stage disease. J. Am. Coll. Surg., 208, 341-347.
49. Grande, E., Inghelmann, R., Francisci, S., Verdecchia, A., Micheli, A., Baili, P., Capocaccia, R. and De Angelis, R. (2007) Regional estimates of all cancer malignancies in Italy. Tumori, 93, 345-351.
50. Hakulinen, T. and Tenkanen, L. (1987) Regression analysis of relative survival rates. Appl. Statist., 36, 309-317.
51. Harrington, D. P. and Fleming, T. R. (1982) A class of rank test procedures for censored survival data. Biometrika, 69, 553-566.
52. Holli, K. and Isola, J. (1997) Effect of age on the survival of breast cancer patients. Eur. J. Cancer, 33, 425-428.
53. Kellen, E., Vansant, G., Christiaens, M. R., Neven, P. and Van, L. E. (2009) Lifestyle changes and breast cancer prognosis: a review. Brst Cancer Res Trtmnt, 114, 13-22.
54. Lagerlund, M., Bellocco, R., Karlsson, P., Tejler, G. and Lambe, M. (2005) Socio-economic factors and breast cancer survival-a population-based cohort study (Sweden). Cancer Causes Contr., 16, 419-430.
55. Langford, A. and Johnson, B. (2009) Social inequalities in adult female mortality by the National Statistics Socio-economic Classification, England and Wales, 2001-03. Hlth Statist. Q., 42, 6-21.
56. Lantz, P. M., Mujahid, M., Schwartz, K., Janz, N. K., Fagerlin, A., Salem, B., Liu, L., Deapen, D. and Katz, S. J. (2006) The influence of race, ethnicity, and individual socioeconomic factors on breast cancer stage at diagnosis. Am. J. Publ. Hlth, 96, 2173-2178.
57. Lawson, A.B. (2009) Bayesian Disease Mapping: Hierarchical Modeling of Spatial Epidemiology. New York: CRC Press.
58. Lawson, A. B., Browne, W. J. and Vidal-Rodeiro, C. L. (2003) Disease Mapping with WinBUGS and MLwiN. Chichester: Wiley.
59. Lopez-Abente, G., Ramis, R., Pollán, M., Aragonés, N., Pérez-Gómez, B., Gómez-Barroso, D., Carrasco, J. M., Lope, V., García-Pérez, J., Boldo, E. and García-Mendizabal, M. J. (2006) Municipal Atlas of Cancer Mortality in Spain 1989-1998 (in Spanish). Madrid: Instituto de Salud Carlos III.
60. Louwman, W. J., van de Poll-France, L. V., Frachebound, J., Roukema, J. W. and Coebergh, J. W. (2007) Impact of a programme of mass mammography screening for breast cancer on socio-economic variation in survival: a population-based study. Brst Cancer Res Trtmnt, 105, 369-375.
61. Lunn, D. J., Thomas, A., Best, N. and Spiegelhalter, D. (2000) WinBUGS-a Bayesian modelling framework: concepts., structure, and extensibility. Statist. Comput., 10, 325-337.
62. Martino, S., Akekar, R. and Rue, H. (2010) Approximate Bayesian inference for survival models. Statistics Preprint 3/2010. Norwegian University of Science and Technology, Trondheim. (Available from).
63. Martos, M. C., Saurina, C., Feja, C., Saez, M., Burriel, M. C., Barceló, M. A., Gómez, P., Renart, G., Alcalá, T. and Marcos-Gragera, R. (2009) Accurately estimating breast cancer survival in Spain: cross-matching local cancer registries with the National Death Index. Pan Am. J. Publ. Hlth, 26, 51-54.
64. Mollié, A. (1996) Bayesian mapping of disease. In Markov Chain Monte Carlo in Practice (eds W. R. Gilks, S. Richardson and D. J. Spiegelhalter), pp. 359-379. New York: Chapman and Hall.
65. Monnet, E., Faivre, J., Raymond, L. and Garau, I. (1998) Comparability of colorectal cancer survival data in three European population-based registries. Eur. J. Cancer Prevn, 7, 127-134.
66. Office for National Statistics (2008) Cancer survival lower in deprived areas. News Release, Sept. 4th.
67. Percy, C., Stanek, E. and Gloecker, L. (1981) Accuracy of cancer death certificates and its effects on cancer mortality statistics. Am. J. Publ. Hlth, 71, 242-250.
68. Ponsà, J. A., Cutillas, S., Elías, A., Fusté, J., Lacasa, C., Olivet, M. and Violan, C. (2003) The reform of primary healthcare (in Catalan). Fulls Econ. Sist. Sanit., 37, 8-15.
69. Rachet, B. and Coleman, M. P. (2004) Cancer Survival Indicators for the National Health Service in England: Exploration of Alternative Geographical Units of Analysis-the Primary Care Organization and the Strategic Health Authority. London: London School of Hygiene and Tropical Medicine.
70. R Development Core Team (2008) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
71. Richardson, S., Thomson, A., Best, N. G. and Elliot, P. (2004) Interpreting posterior relative risk estimates in disease-mapping studies. Environ. Hlth Perspect., 112, 1016-1025.
72. Robert, S. A., Strombom, I., Trentham-Dietz, A., Hampton, J. M., McElroy, J. A., Newcomb, P. A. and Remington, P. L. (2004) Socioeconomic risk factors for breast cancer: distinguishing individual- and community-level effects. Epidemiology, 15, 442-450.
73. Saez, M., Marcos-Gragera, R., Barceló, M. A., Martos, M. C., Saurina, C., Feja, C., Arribas, F., Alcalá, T. and Renart, G. (2008) Geographical variation in relative survival from female breast cancer in the Girona Health Region, Spain. Clinical Oncology Society of Australia-Internal Association of Cancer Registries Jt Scient. Meet., Sydney, Nov. 18th-20th.
74. Saez, M. and Saurina, C. (2007) Spatial Statistics and Epidemiology (in Spanish). Girona: Documenta Universitaria.
75. Saez, M., Saurina, C., Coenders, G. and González-Raya, S. (2006) Use of primary health care services according to the different degrees of obesity in the Girona Health Region, Spain. Hlth Econ., 15, 173-193.
76. Sankila, R., Martos-Jiménez, M. C., Miljus, D., Pritchard-Jones, K., Steliarova-Foucher, E. and Stiller, C. (2006) Geographical comparison of cancer survival in European children (1988-1997). Eur. J. Cancer, 42, 1972-1980.
77. Sant, M., Allemani, C., De Angelis, R., Carbone, A., de Sanjosé, S., Gianni, A. M., Giraldo, P., Marchesi, F., Marcos-Gragera, R., Martos-Jiménez, C., Maynadié, M., Raphael, M., Berrino, F. and EUROCARE-3 Working Group (2008) Influence of morphology on survival for non-Hodgkin lymphoma in Europe and the United States. Eur. J. Cancer, 44, 579-587.
78. Sant, M., Capocaccia, R., Verdecchia, A., Estève, J., Gatta, G., Micheli, A., Coleman, M. P. and Berrino, F. (1998) Survival of women with breast cancer in Europe: variation with age, year of diagnosis and country: the EUROCARE Working Group. Int. J. Cancer, 77, 679-683.
79. Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002) Bayesian measures of model complexity and fit (with discussion). J. R. Statist. Soc. B, 64, 583-639.
80. Stracci, F., La Rosa, F., Falsettini, E., Ricci, E., Aristei, C., Bellezza, G., Bolis, G. B., Fenocchio, D., Gori, S., Rulli, A. and Mastrandrea, V. (2005) A population survival model for breast cancer. Breast, 14, 94-102.
81. Therneau, T. M., Grambsch, P. M. and Fleming, T. R. (1990) Martingale-based residuals for survival models. Biometrika, 77, 147-160.
82. Twelves, C. J., Thomson, C. S., Dewar, J. A. and Brewster, D. H. (2001) Variation in survival of women with breast cancer: Health Board remains a factor at 10 years. Br. J. Cancer, 85, 637-640.
83. Tyczynski, J. E., Bray, F. and Parkin, D. M. (2002) Breast cancer in Europe. Cancer Fact Sheet 2. European Network of Cancer Registries.
84. Unitat d'Epidemiologia i Registre de Càncer de Girona (2005) Cancer Figures in Girona 2002 (in Catalan). Girona: Unitat d'Epidemiologia i Registre de Càncer de Girona.
85. Unitat d'Epidemiologia i Registre de Càncer de Girona (2009) Cancer in Girona 2003-2004 (in Catalan). Girona: Unitat d'Epidemiologia i Registre de Càncer de Girona.
86. Whitehead, J. (1980) Fitting Cox's regression model to survival data using GLIM. Appl. Statist., 29, 268-275.
87. World Health Organization (2009) International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Version for 2007. Geneva: World Health Organization. (Available from).
88. Yu, X. Q., O'Connell, D. L., Gibberd, R. W., Abrahamowicz, M. and Armstrong, B. K. (2008) Misclassification of colorectal cancer stage and area variation in survival. Int. J. Cancer, 122, 398-402.
89. Yu, X. Q., O'Connell, D. L., Gibberd, R. W. and Armstrong, B. (2005) A population-based study from New South Wales, Australia 1996-2001: area variation in survival from colorectal cancer. Eur. J. Cancer, 41, 2715-2721.
90. Yu, X. Q., O'Connell, D. L., Gibberd, R. W., Smith, D. P., Dickman, P. W. and Armstrong, B. K. (2004) Estimating regional variation in cancer survival: a tool for improving cancer care. Cancer Causes Contr., 15, 611-618.
91. Zhou, H., Lawson, A. B., Hebert, J. R., Slate, E. H. and Hill, E. G. (2008) Joint spatial survival modelling for the age at diagnosis and the vital outcome of prostate cancer. Statist. Med., 27, 3612-3628.