Daniel Redondo-Sánchez1,2,3, Miguel Rodríguez-Barranco4,5,6, Alberto Ameijide7, Francisco Javier Alonso8, Pablo Fernández-Navarro3,9, Jose Juan Jiménez-Moleón2,3,10, María-José Sánchez1,2,3,10. 1. Granada Cancer Registry, Andalusian School of Public Health (EASP), Campus Universitario de Cartuja, C/Cuesta del Observatorio 4, 18011, Granada, Spain. 2. Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), University of Granada, Granada, Spain. 3. CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain. 4. Granada Cancer Registry, Andalusian School of Public Health (EASP), Campus Universitario de Cartuja, C/Cuesta del Observatorio 4, 18011, Granada, Spain. miguel.rodriguez.barranco.easp@juntadeandalucia.es. 5. Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), University of Granada, Granada, Spain. miguel.rodriguez.barranco.easp@juntadeandalucia.es. 6. CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain. miguel.rodriguez.barranco.easp@juntadeandalucia.es. 7. Tarragona Cancer Registry, Foundation Society for Cancer Research and Prevention (FUNCA), Pere Virgili Health Research Institute (IISPV), Reus, Spain. 8. Department of Statistics, Faculty of Sciences, University of Granada, Granada, Spain. 9. Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain. 10. Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain.
Abstract
BACKGROUND: Population-based cancer registries are required to calculate cancer incidence in a geographical area, and several methods have been developed to obtain estimations of cancer incidence in areas not covered by a cancer registry. However, an extended analysis of those methods in order to confirm their validity is still needed. METHODS: We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio (IMR), the IMR method. Using the previous 15-year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004-2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004-2013 in Granada. A goodness-of-fit (GOF) indicator was formulated to determine the best estimation scenario. RESULTS: A total of 39,848 cancer incidence cases and 43,884 deaths due to cancer were included. The relative differences between the observed and predicted numbers of cancer cases were less than 10% for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women. The linear assumption was better for lung and ovarian cancers in women and prostate cancer in men. In the best scenario, the mean absolute percentage error was 6% in men and 4% in women for overall cancer. Female breast cancer and prostate cancer obtained the worst GOF results in all scenarios. CONCLUSION: A comparison with a historical time series of real data in a population-based cancer registry indicated that the IMR method is a valid tool for the estimation of cancer incidence. The goodness-of-fit indicator proposed can help select the best assumption for the IMR based on a statistical argument.
BACKGROUND: Population-based cancer registries are required to calculate cancer incidence in a geographical area, and several methods have been developed to obtain estimations of cancer incidence in areas not covered by a cancer registry. However, an extended analysis of those methods in order to confirm their validity is still needed. METHODS: We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio (IMR), the IMR method. Using the previous 15-year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004-2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004-2013 in Granada. A goodness-of-fit (GOF) indicator was formulated to determine the best estimation scenario. RESULTS: A total of 39,848 cancer incidence cases and 43,884 deaths due to cancer were included. The relative differences between the observed and predicted numbers of cancer cases were less than 10% for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women. The linear assumption was better for lung and ovarian cancers in women and prostate cancer in men. In the best scenario, the mean absolute percentage error was 6% in men and 4% in women for overall cancer. Female breast cancer and prostate cancer obtained the worst GOF results in all scenarios. CONCLUSION: A comparison with a historical time series of real data in a population-based cancer registry indicated that the IMR method is a valid tool for the estimation of cancer incidence. The goodness-of-fit indicator proposed can help select the best assumption for the IMR based on a statistical argument.
Entities:
Keywords:
Cancer incidence; Estimation; Goodness-of-fit; Mortality-to-incidence ratio; Validation
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