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Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence: Part II: evaluation of spatiote...
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| Zeitschriftentitel: | Cancer |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , , , , |
| In: | Cancer, 118, 2012, 4, S. 1100-1109 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
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| Schlagwörter: |
| author_facet |
Zhu, Li Pickle, Linda W. Ghosh, Kaushik Naishadham, Deepa Portier, Kenneth Chen, Huann‐Sheng Kim, Hyune‐Ju Zou, Zhaohui Cucinelli, James Kohler, Betsy Edwards, Brenda K. King, Jessica Feuer, Eric J. Jemal, Ahmedin Zhu, Li Pickle, Linda W. Ghosh, Kaushik Naishadham, Deepa Portier, Kenneth Chen, Huann‐Sheng Kim, Hyune‐Ju Zou, Zhaohui Cucinelli, James Kohler, Betsy Edwards, Brenda K. King, Jessica Feuer, Eric J. Jemal, Ahmedin |
|---|---|
| author |
Zhu, Li Pickle, Linda W. Ghosh, Kaushik Naishadham, Deepa Portier, Kenneth Chen, Huann‐Sheng Kim, Hyune‐Ju Zou, Zhaohui Cucinelli, James Kohler, Betsy Edwards, Brenda K. King, Jessica Feuer, Eric J. Jemal, Ahmedin |
| spellingShingle |
Zhu, Li Pickle, Linda W. Ghosh, Kaushik Naishadham, Deepa Portier, Kenneth Chen, Huann‐Sheng Kim, Hyune‐Ju Zou, Zhaohui Cucinelli, James Kohler, Betsy Edwards, Brenda K. King, Jessica Feuer, Eric J. Jemal, Ahmedin Cancer Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence Cancer Research Oncology |
| author_sort |
zhu, li |
| spelling |
Zhu, Li Pickle, Linda W. Ghosh, Kaushik Naishadham, Deepa Portier, Kenneth Chen, Huann‐Sheng Kim, Hyune‐Ju Zou, Zhaohui Cucinelli, James Kohler, Betsy Edwards, Brenda K. King, Jessica Feuer, Eric J. Jemal, Ahmedin 0008-543X 1097-0142 Wiley Cancer Research Oncology http://dx.doi.org/10.1002/cncr.27405 <jats:title>Abstract</jats:title><jats:sec><jats:title>BACKGROUND.</jats:title><jats:p>The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases.</jats:p></jats:sec><jats:sec><jats:title>METHODS.</jats:title><jats:p>Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age‐specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay‐adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted.</jats:p></jats:sec><jats:sec><jats:title>RESULTS.</jats:title><jats:p>The differences between the spatiotemporal model‐estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS.</jats:title><jats:p>The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts. Cancer 2012;. © 2012 American Cancer Society.</jats:p></jats:sec> Part II: evaluation of spatiotemporal projection methods for incidence Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence Cancer |
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Wiley, 2012 |
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Wiley, 2012 |
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| title_sub |
Part II: evaluation of spatiotemporal projection methods for incidence |
| title |
Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_unstemmed |
Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_full |
Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_fullStr |
Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_full_unstemmed |
Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_short |
Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_sort |
predicting us‐ and state‐level cancer counts for the current calendar year : part ii: evaluation of spatiotemporal projection methods for incidence |
| topic |
Cancer Research Oncology |
| url |
http://dx.doi.org/10.1002/cncr.27405 |
| publishDate |
2012 |
| physical |
1100-1109 |
| description |
<jats:title>Abstract</jats:title><jats:sec><jats:title>BACKGROUND.</jats:title><jats:p>The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases.</jats:p></jats:sec><jats:sec><jats:title>METHODS.</jats:title><jats:p>Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age‐specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay‐adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted.</jats:p></jats:sec><jats:sec><jats:title>RESULTS.</jats:title><jats:p>The differences between the spatiotemporal model‐estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS.</jats:title><jats:p>The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts. Cancer 2012;. © 2012 American Cancer Society.</jats:p></jats:sec> |
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| author | Zhu, Li, Pickle, Linda W., Ghosh, Kaushik, Naishadham, Deepa, Portier, Kenneth, Chen, Huann‐Sheng, Kim, Hyune‐Ju, Zou, Zhaohui, Cucinelli, James, Kohler, Betsy, Edwards, Brenda K., King, Jessica, Feuer, Eric J., Jemal, Ahmedin |
| author_facet | Zhu, Li, Pickle, Linda W., Ghosh, Kaushik, Naishadham, Deepa, Portier, Kenneth, Chen, Huann‐Sheng, Kim, Hyune‐Ju, Zou, Zhaohui, Cucinelli, James, Kohler, Betsy, Edwards, Brenda K., King, Jessica, Feuer, Eric J., Jemal, Ahmedin, Zhu, Li, Pickle, Linda W., Ghosh, Kaushik, Naishadham, Deepa, Portier, Kenneth, Chen, Huann‐Sheng, Kim, Hyune‐Ju, Zou, Zhaohui, Cucinelli, James, Kohler, Betsy, Edwards, Brenda K., King, Jessica, Feuer, Eric J., Jemal, Ahmedin |
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| description | <jats:title>Abstract</jats:title><jats:sec><jats:title>BACKGROUND.</jats:title><jats:p>The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases.</jats:p></jats:sec><jats:sec><jats:title>METHODS.</jats:title><jats:p>Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age‐specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay‐adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted.</jats:p></jats:sec><jats:sec><jats:title>RESULTS.</jats:title><jats:p>The differences between the spatiotemporal model‐estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS.</jats:title><jats:p>The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts. Cancer 2012;. © 2012 American Cancer Society.</jats:p></jats:sec> |
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| imprint_str_mv | Wiley, 2012 |
| institution | DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3 |
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| spelling | Zhu, Li Pickle, Linda W. Ghosh, Kaushik Naishadham, Deepa Portier, Kenneth Chen, Huann‐Sheng Kim, Hyune‐Ju Zou, Zhaohui Cucinelli, James Kohler, Betsy Edwards, Brenda K. King, Jessica Feuer, Eric J. Jemal, Ahmedin 0008-543X 1097-0142 Wiley Cancer Research Oncology http://dx.doi.org/10.1002/cncr.27405 <jats:title>Abstract</jats:title><jats:sec><jats:title>BACKGROUND.</jats:title><jats:p>The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases.</jats:p></jats:sec><jats:sec><jats:title>METHODS.</jats:title><jats:p>Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age‐specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay‐adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted.</jats:p></jats:sec><jats:sec><jats:title>RESULTS.</jats:title><jats:p>The differences between the spatiotemporal model‐estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS.</jats:title><jats:p>The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts. Cancer 2012;. © 2012 American Cancer Society.</jats:p></jats:sec> Part II: evaluation of spatiotemporal projection methods for incidence Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence Cancer |
| spellingShingle | Zhu, Li, Pickle, Linda W., Ghosh, Kaushik, Naishadham, Deepa, Portier, Kenneth, Chen, Huann‐Sheng, Kim, Hyune‐Ju, Zou, Zhaohui, Cucinelli, James, Kohler, Betsy, Edwards, Brenda K., King, Jessica, Feuer, Eric J., Jemal, Ahmedin, Cancer, Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence, Cancer Research, Oncology |
| title | Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_full | Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_fullStr | Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_full_unstemmed | Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_short | Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| title_sort | predicting us‐ and state‐level cancer counts for the current calendar year : part ii: evaluation of spatiotemporal projection methods for incidence |
| title_sub | Part II: evaluation of spatiotemporal projection methods for incidence |
| title_unstemmed | Predicting US‐ and state‐level cancer counts for the current calendar year : Part II: evaluation of spatiotemporal projection methods for incidence |
| topic | Cancer Research, Oncology |
| url | http://dx.doi.org/10.1002/cncr.27405 |