Bayesian estimates of the incidence of rare cancers in Europe

L. Botta, R. Capocaccia, A. Trama, C. Herrmann, D. Salmerón, R. De Angelis, S. Mallone, E. Bidoli, R. Marcos-Gragera, D. Dudek-Godeau, G. Gatta, R. Cleries, The RACECAREnet Working group

Research output: Contribution to journalArticle

Abstract

Background: The RARECAREnet project has updated the estimates of the burden of the 198 rare cancers in each European country. Suspecting that scant data could affect the reliability of statistical analysis, we employed a Bayesian approach to estimate the incidence of these cancers. Methods: We analyzed about 2,000,000 rare cancers diagnosed in 2000–2007 provided by 83 population-based cancer registries from 27 European countries. We considered European incidence rates (IRs), calculated over all the data available in RARECAREnet, as a valid a priori to merge with country-specific observed data. Therefore we provided (1) Bayesian estimates of IRs and the yearly numbers of cases of rare cancers in each country; (2) the expected time (T) in years needed to observe one new case; and (3) practical criteria to decide when to use the Bayesian approach. Results: Bayesian and classical estimates did not differ much; substantial differences (>10%) ranged from 77 rare cancers in Iceland to 14 in England. The smaller the population the larger the number of rare cancers needing a Bayesian approach. Bayesian estimates were useful for cancers with fewer than 150 observed cases in a country during the study period; this occurred mostly when the population of the country is small. Conclusion: For the first time the Bayesian estimates of IRs and the yearly expected numbers of cases for each rare cancer in each individual European country were calculated. Moreover, the indicator T is useful to convey incidence estimates for exceptionally rare cancers and in small countries; it far exceeds the professional lifespan of a medical doctor. © 2018 Elsevier Ltd
Original languageEnglish
Pages (from-to)95-100
Number of pages6
JournalCancer Epidemiology
Volume54
DOIs
Publication statusPublished - 2018

Fingerprint

Incidence
Neoplasms
Bayes Theorem
Population
Iceland
England
Registries

Keywords

  • Bayesian analysis
  • European countries
  • Incidence
  • Population-based cancer registries
  • Rare cancer
  • Article
  • Bayes theorem
  • cancer incidence
  • cancer registry
  • colloid carcinoma
  • Europe
  • European
  • female
  • human
  • major clinical study
  • malignant neoplasm
  • ovary adenocarcinoma
  • population research
  • priority journal
  • rare disease
  • trachea carcinoma

Cite this

Botta, L., Capocaccia, R., Trama, A., Herrmann, C., Salmerón, D., De Angelis, R., ... group, T. RACECARE. W. (2018). Bayesian estimates of the incidence of rare cancers in Europe. Cancer Epidemiology, 54, 95-100. https://doi.org/10.1016/j.canep.2018.04.003

Bayesian estimates of the incidence of rare cancers in Europe. / Botta, L.; Capocaccia, R.; Trama, A.; Herrmann, C.; Salmerón, D.; De Angelis, R.; Mallone, S.; Bidoli, E.; Marcos-Gragera, R.; Dudek-Godeau, D.; Gatta, G.; Cleries, R.; group, The RACECAREnet Working.

In: Cancer Epidemiology, Vol. 54, 2018, p. 95-100.

Research output: Contribution to journalArticle

Botta, L, Capocaccia, R, Trama, A, Herrmann, C, Salmerón, D, De Angelis, R, Mallone, S, Bidoli, E, Marcos-Gragera, R, Dudek-Godeau, D, Gatta, G, Cleries, R & group, TRACECAREW 2018, 'Bayesian estimates of the incidence of rare cancers in Europe', Cancer Epidemiology, vol. 54, pp. 95-100. https://doi.org/10.1016/j.canep.2018.04.003
Botta, L. ; Capocaccia, R. ; Trama, A. ; Herrmann, C. ; Salmerón, D. ; De Angelis, R. ; Mallone, S. ; Bidoli, E. ; Marcos-Gragera, R. ; Dudek-Godeau, D. ; Gatta, G. ; Cleries, R. ; group, The RACECAREnet Working. / Bayesian estimates of the incidence of rare cancers in Europe. In: Cancer Epidemiology. 2018 ; Vol. 54. pp. 95-100.
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title = "Bayesian estimates of the incidence of rare cancers in Europe",
abstract = "Background: The RARECAREnet project has updated the estimates of the burden of the 198 rare cancers in each European country. Suspecting that scant data could affect the reliability of statistical analysis, we employed a Bayesian approach to estimate the incidence of these cancers. Methods: We analyzed about 2,000,000 rare cancers diagnosed in 2000–2007 provided by 83 population-based cancer registries from 27 European countries. We considered European incidence rates (IRs), calculated over all the data available in RARECAREnet, as a valid a priori to merge with country-specific observed data. Therefore we provided (1) Bayesian estimates of IRs and the yearly numbers of cases of rare cancers in each country; (2) the expected time (T) in years needed to observe one new case; and (3) practical criteria to decide when to use the Bayesian approach. Results: Bayesian and classical estimates did not differ much; substantial differences (>10{\%}) ranged from 77 rare cancers in Iceland to 14 in England. The smaller the population the larger the number of rare cancers needing a Bayesian approach. Bayesian estimates were useful for cancers with fewer than 150 observed cases in a country during the study period; this occurred mostly when the population of the country is small. Conclusion: For the first time the Bayesian estimates of IRs and the yearly expected numbers of cases for each rare cancer in each individual European country were calculated. Moreover, the indicator T is useful to convey incidence estimates for exceptionally rare cancers and in small countries; it far exceeds the professional lifespan of a medical doctor. {\circledC} 2018 Elsevier Ltd",
keywords = "Bayesian analysis, European countries, Incidence, Population-based cancer registries, Rare cancer, Article, Bayes theorem, cancer incidence, cancer registry, colloid carcinoma, Europe, European, female, human, major clinical study, malignant neoplasm, ovary adenocarcinoma, population research, priority journal, rare disease, trachea carcinoma",
author = "L. Botta and R. Capocaccia and A. Trama and C. Herrmann and D. Salmer{\'o}n and {De Angelis}, R. and S. Mallone and E. Bidoli and R. Marcos-Gragera and D. Dudek-Godeau and G. Gatta and R. Cleries and group, {The RACECAREnet Working}",
note = "Cited By :2 Export Date: 12 April 2019 Correspondence Address: Botta, L.; Evaluative Epidemiology Unit, Fondazione IRCCS Istituto Nazionale TumoriItaly; email: Laura.botta@istitutotumori.mi.it Funding details: Consumers, Health, Agriculture and Food Executive Agency, 2000111201 Funding details: European Commission Funding text 1: This work was supported by European Commission through the Consumers, Health, Agriculture and Food Executive Agency (Chafea) (grant number 2000111201 ). Project title ‘Information network on rare cancers’ – RARECARENet. The funders had no role in study design, data collection, analysis or interpretation, or writing of the report. The corresponding author had full access to all data and had final responsibility for the decision to submit for publication. References: Gatta, G., van der Zwan, J.M., Casali, P.G., Siesling, S., Dei Tos, A.P., Kunkler, I., Rare cancers are not so rare: the rare cancer burden in Europe (2011) Eur J. Cancer, 47, pp. 2493-2511; http://www.RARECAREnet.eu/RARECAREnet/index.php/cancerlist, RARECAREnet project, list of entities, (Accessed 10 June 2016); Gatta, G., Capocaccia, R., Botta, L., Mallone, S., De Angelis, R., Ardanaz, E., Burden, time trends and centralized treatment of rare tumors: a European perspective. The RARECAREnet project (2017) Lancet Oncol., 18 (August (8)), pp. 1022-1039; Bashir, S.A., Est{\`e}ve, J., Projecting cancer incidence and mortality using Bayesian age-period-cohort models (2001) J. Epidemiol. Biostat., 6 (3), pp. 287-296; Bootstrap, C.M., Investigation of the stability of disease mapping of Bayesian cancer relative risk estimations (2004) Eur. J. Epidemiol., 19 (8), pp. 761-768; Dunson, D.B., Commentary: practical advantages of Bayesian analysis of epidemiologic data (2001) Am. J. Epidemiol., 153 (June (12)), pp. 1222-1226; Bogaerts, J., Sydes, M.R., Keat, N., McConnell, A., Benson, A., Ho, A., Clinical trial designs for rare diseases: studies developed and discussed by the International rare cancers initiative (2015) Eur J. Cancer, 51 (February (3)), pp. 271-281; Tsutakawa, R.K., Estimation of cancer mortality rates: a Bayesian analysis of small frequencies (1985) Biometrics, 41 (March (1)), pp. 69-79; Rossi, S., Baili, P., Capocaccia, R., Caldora, M., Carrani, E., Minicozzi, P., The EUROCARE-5 study on cancer survival in Europe 1999-2007: database, quality checks and statistical analysis methods (2015) Eur. J. Cancer, 51 (October (15)), pp. 2104-2119; Clayton, D., Kaldor, J., Empirical Bayes estimates of age-standardized relative risks for use in disease mapping (1987) Biometrics, 43 (3), pp. 671-681; Blangiardo, M., Cameletti, M., Baio, G., Rue, H., Spatial and spatio-temporal models with R-INLA (2013) Spatial Spatio-temporal Epidemiol., 4, pp. 33-49; Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D., WinBUGS a Bayesian modelling framework: concepts, structure, and extensibility (2000) Statist. Comput., 10, pp. 325-337; Chen, Z., McGee, M., Bayesian, A., Approach to zero-numerator problems using hierarchical models (2008) J. Data Sci., pp. 261-268; Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B., Bayesian Data Analysis (2003), second edition Chapman and Hall London; Winkler, R.L., Smith, J.E., Fryback, D., The role of informative priors in zero-numerator problems: being conservative versus being candid (2002) Am. Stat., 56 (1); Trama, A., Marcos-Gragera, R., S{\'a}nchez P{\'e}rez, M.J., van der Zwan, J.M., Bouchardy, C., Melchor, J.M., Data quality in rare cancers registration: the report of the RARECARE data quality study (2017) Tumori, 103 (1), pp. 22-32",
year = "2018",
doi = "10.1016/j.canep.2018.04.003",
language = "English",
volume = "54",
pages = "95--100",
journal = "Cancer Epidemiology",
issn = "1877-7821",
publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Bayesian estimates of the incidence of rare cancers in Europe

AU - Botta, L.

AU - Capocaccia, R.

AU - Trama, A.

AU - Herrmann, C.

AU - Salmerón, D.

AU - De Angelis, R.

AU - Mallone, S.

AU - Bidoli, E.

AU - Marcos-Gragera, R.

AU - Dudek-Godeau, D.

AU - Gatta, G.

AU - Cleries, R.

AU - group, The RACECAREnet Working

N1 - Cited By :2 Export Date: 12 April 2019 Correspondence Address: Botta, L.; Evaluative Epidemiology Unit, Fondazione IRCCS Istituto Nazionale TumoriItaly; email: Laura.botta@istitutotumori.mi.it Funding details: Consumers, Health, Agriculture and Food Executive Agency, 2000111201 Funding details: European Commission Funding text 1: This work was supported by European Commission through the Consumers, Health, Agriculture and Food Executive Agency (Chafea) (grant number 2000111201 ). Project title ‘Information network on rare cancers’ – RARECARENet. The funders had no role in study design, data collection, analysis or interpretation, or writing of the report. The corresponding author had full access to all data and had final responsibility for the decision to submit for publication. References: Gatta, G., van der Zwan, J.M., Casali, P.G., Siesling, S., Dei Tos, A.P., Kunkler, I., Rare cancers are not so rare: the rare cancer burden in Europe (2011) Eur J. Cancer, 47, pp. 2493-2511; http://www.RARECAREnet.eu/RARECAREnet/index.php/cancerlist, RARECAREnet project, list of entities, (Accessed 10 June 2016); Gatta, G., Capocaccia, R., Botta, L., Mallone, S., De Angelis, R., Ardanaz, E., Burden, time trends and centralized treatment of rare tumors: a European perspective. The RARECAREnet project (2017) Lancet Oncol., 18 (August (8)), pp. 1022-1039; Bashir, S.A., Estève, J., Projecting cancer incidence and mortality using Bayesian age-period-cohort models (2001) J. Epidemiol. Biostat., 6 (3), pp. 287-296; Bootstrap, C.M., Investigation of the stability of disease mapping of Bayesian cancer relative risk estimations (2004) Eur. J. Epidemiol., 19 (8), pp. 761-768; Dunson, D.B., Commentary: practical advantages of Bayesian analysis of epidemiologic data (2001) Am. J. Epidemiol., 153 (June (12)), pp. 1222-1226; Bogaerts, J., Sydes, M.R., Keat, N., McConnell, A., Benson, A., Ho, A., Clinical trial designs for rare diseases: studies developed and discussed by the International rare cancers initiative (2015) Eur J. Cancer, 51 (February (3)), pp. 271-281; Tsutakawa, R.K., Estimation of cancer mortality rates: a Bayesian analysis of small frequencies (1985) Biometrics, 41 (March (1)), pp. 69-79; Rossi, S., Baili, P., Capocaccia, R., Caldora, M., Carrani, E., Minicozzi, P., The EUROCARE-5 study on cancer survival in Europe 1999-2007: database, quality checks and statistical analysis methods (2015) Eur. J. Cancer, 51 (October (15)), pp. 2104-2119; Clayton, D., Kaldor, J., Empirical Bayes estimates of age-standardized relative risks for use in disease mapping (1987) Biometrics, 43 (3), pp. 671-681; Blangiardo, M., Cameletti, M., Baio, G., Rue, H., Spatial and spatio-temporal models with R-INLA (2013) Spatial Spatio-temporal Epidemiol., 4, pp. 33-49; Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D., WinBUGS a Bayesian modelling framework: concepts, structure, and extensibility (2000) Statist. Comput., 10, pp. 325-337; Chen, Z., McGee, M., Bayesian, A., Approach to zero-numerator problems using hierarchical models (2008) J. Data Sci., pp. 261-268; Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B., Bayesian Data Analysis (2003), second edition Chapman and Hall London; Winkler, R.L., Smith, J.E., Fryback, D., The role of informative priors in zero-numerator problems: being conservative versus being candid (2002) Am. Stat., 56 (1); Trama, A., Marcos-Gragera, R., Sánchez Pérez, M.J., van der Zwan, J.M., Bouchardy, C., Melchor, J.M., Data quality in rare cancers registration: the report of the RARECARE data quality study (2017) Tumori, 103 (1), pp. 22-32

PY - 2018

Y1 - 2018

N2 - Background: The RARECAREnet project has updated the estimates of the burden of the 198 rare cancers in each European country. Suspecting that scant data could affect the reliability of statistical analysis, we employed a Bayesian approach to estimate the incidence of these cancers. Methods: We analyzed about 2,000,000 rare cancers diagnosed in 2000–2007 provided by 83 population-based cancer registries from 27 European countries. We considered European incidence rates (IRs), calculated over all the data available in RARECAREnet, as a valid a priori to merge with country-specific observed data. Therefore we provided (1) Bayesian estimates of IRs and the yearly numbers of cases of rare cancers in each country; (2) the expected time (T) in years needed to observe one new case; and (3) practical criteria to decide when to use the Bayesian approach. Results: Bayesian and classical estimates did not differ much; substantial differences (>10%) ranged from 77 rare cancers in Iceland to 14 in England. The smaller the population the larger the number of rare cancers needing a Bayesian approach. Bayesian estimates were useful for cancers with fewer than 150 observed cases in a country during the study period; this occurred mostly when the population of the country is small. Conclusion: For the first time the Bayesian estimates of IRs and the yearly expected numbers of cases for each rare cancer in each individual European country were calculated. Moreover, the indicator T is useful to convey incidence estimates for exceptionally rare cancers and in small countries; it far exceeds the professional lifespan of a medical doctor. © 2018 Elsevier Ltd

AB - Background: The RARECAREnet project has updated the estimates of the burden of the 198 rare cancers in each European country. Suspecting that scant data could affect the reliability of statistical analysis, we employed a Bayesian approach to estimate the incidence of these cancers. Methods: We analyzed about 2,000,000 rare cancers diagnosed in 2000–2007 provided by 83 population-based cancer registries from 27 European countries. We considered European incidence rates (IRs), calculated over all the data available in RARECAREnet, as a valid a priori to merge with country-specific observed data. Therefore we provided (1) Bayesian estimates of IRs and the yearly numbers of cases of rare cancers in each country; (2) the expected time (T) in years needed to observe one new case; and (3) practical criteria to decide when to use the Bayesian approach. Results: Bayesian and classical estimates did not differ much; substantial differences (>10%) ranged from 77 rare cancers in Iceland to 14 in England. The smaller the population the larger the number of rare cancers needing a Bayesian approach. Bayesian estimates were useful for cancers with fewer than 150 observed cases in a country during the study period; this occurred mostly when the population of the country is small. Conclusion: For the first time the Bayesian estimates of IRs and the yearly expected numbers of cases for each rare cancer in each individual European country were calculated. Moreover, the indicator T is useful to convey incidence estimates for exceptionally rare cancers and in small countries; it far exceeds the professional lifespan of a medical doctor. © 2018 Elsevier Ltd

KW - Bayesian analysis

KW - European countries

KW - Incidence

KW - Population-based cancer registries

KW - Rare cancer

KW - Article

KW - Bayes theorem

KW - cancer incidence

KW - cancer registry

KW - colloid carcinoma

KW - Europe

KW - European

KW - female

KW - human

KW - major clinical study

KW - malignant neoplasm

KW - ovary adenocarcinoma

KW - population research

KW - priority journal

KW - rare disease

KW - trachea carcinoma

U2 - 10.1016/j.canep.2018.04.003

DO - 10.1016/j.canep.2018.04.003

M3 - Article

VL - 54

SP - 95

EP - 100

JO - Cancer Epidemiology

JF - Cancer Epidemiology

SN - 1877-7821

ER -