On predicting cancer mortality using ANOVA-type P-spline models
Extrapolating cancer mortality trends can be very valuable as a tool to predict cancer burden. National Health Agencies use different models to figure out future evolution of cancer, but they mainly work at national level. However, developed countries are divided into different regions with their ow...
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| Published in | Revstat Vol. 13; no. 1; p. 21 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Instituto Nacional de Estatistica
01.03.2015
Instituto Nacional de Estatística | Statistics Portugal |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1645-6726 2183-0371 |
| DOI | 10.57805/revstat.v13i1.162 |
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| Abstract | Extrapolating cancer mortality trends can be very valuable as a tool to predict cancer burden. National Health Agencies use different models to figure out future evolution of cancer, but they mainly work at national level. However, developed countries are divided into different regions with their own governments and health care systems, and this should be taken into account. In this paper, an ANOVA-type P-spline model is considered to predict the number of mortality cases in forthcoming years in regions within a country. The model is very interesting as it allows to split the predictions into components representing region-specific features and characteristics common to the whole country. Prediction variability is also calculated to provide prediction intervals. Real data on cancer mortality are used for illustration. Key-Words: region-specific predictions; smoothing and predicting counts; space-time interactions; prostate cancer. AMS Subject Classification: * 62M20, 62G08, 62H11. |
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| AbstractList | Extrapolating cancer mortality trends can be very valuable as a tool to predict cancer burden. National Health Agencies use different models to figure out future evolution of cancer, but they mainly work at national level. However, developed countries are divided into different regions with their own governments and health care systems, and this should be taken into account. In this paper, an ANOVA-type P-spline model is considered to predict the number of mortality cases in forthcoming years in regions within a country. The model is very interesting as it allows to split the predictions into components representing region-specific features and characteristics common to the whole country. Prediction variability is also calculated to provide prediction intervals. Real data on cancer mortality are used for illustration. Extrapolating cancer mortality trends can be very valuable as a tool to predict cancer burden. National Health Agencies use different models to figure out future evolution of cancer, but they mainly work at national level. However, developed countries are divided into different regions with their own governments and health care systems, and this should be taken into account. In this paper, an ANOVA-type P-spline model is considered to predict the number of mortality cases in forthcoming years in regions within a country. The model is very interesting as it allows to split the predictions into components representing region-specific features and characteristics common to the whole country. Prediction variability is also calculated to provide prediction intervals. Real data on cancer mortality are used for illustration. Key-Words: region-specific predictions; smoothing and predicting counts; space-time interactions; prostate cancer. AMS Subject Classification: * 62M20, 62G08, 62H11. |
| Audience | Academic |
| Author | Etxeberria, Jaione Ugarte, Maria Dolores Goicoa, Tomas Militino, Ana F |
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| SubjectTerms | Analysis of variance Cancer Forecasts and trends Mortality Patient outcomes Prognosis prostate cancer region-specific predictions smoothing and predicting counts space-time interactions Spline theory United States |
| Title | On predicting cancer mortality using ANOVA-type P-spline models |
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