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Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time?
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| Zeitschriftentitel: | Cancer Epidemiology, Biomarkers & Prevention |
|---|---|
| Personen und Körperschaften: | , |
| In: | Cancer Epidemiology, Biomarkers & Prevention, 20, 2011, 7, S. 1263-1268 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Association for Cancer Research (AACR)
|
| Schlagwörter: |
| author_facet |
Rosenberg, Philip S. Anderson, William F. Rosenberg, Philip S. Anderson, William F. |
|---|---|
| author |
Rosenberg, Philip S. Anderson, William F. |
| spellingShingle |
Rosenberg, Philip S. Anderson, William F. Cancer Epidemiology, Biomarkers & Prevention Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? Oncology Epidemiology |
| author_sort |
rosenberg, philip s. |
| spelling |
Rosenberg, Philip S. Anderson, William F. 1055-9965 1538-7755 American Association for Cancer Research (AACR) Oncology Epidemiology http://dx.doi.org/10.1158/1055-9965.epi-11-0421 <jats:title>Abstract</jats:title> <jats:p>Standard descriptive methods for the analysis of cancer surveillance data include canonical plots based on the lexis diagram, directly age-standardized rates (ASR), estimated annual percentage change (EAPC), and joinpoint regression. The age-period-cohort (APC) model has been used less often. Here, we argue that it merits much broader use. First, we describe close connections between estimable functions of the model parameters and standard quantities such as the ASR, EAPC, and joinpoints. Estimable functions have the added value of being fully adjusted for period and cohort effects, and generally more precise. Second, the APC model provides the descriptive epidemiologist with powerful new tools, including rigorous statistical methods for comparative analyses, and the ability to project the future burden of cancer. We illustrate these principles by using invasive female breast cancer incidence in the United States, but these concepts apply equally well to other cancer sites for incidence or mortality. Cancer Epidemiol Biomarkers Prev; 20(7); 1263–8. ©2011 AACR.</jats:p> Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? Cancer Epidemiology, Biomarkers & Prevention |
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American Association for Cancer Research (AACR), 2011 |
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| title |
Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_unstemmed |
Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_full |
Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_fullStr |
Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_full_unstemmed |
Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_short |
Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_sort |
age-period-cohort models in cancer surveillance research: ready for prime time? |
| topic |
Oncology Epidemiology |
| url |
http://dx.doi.org/10.1158/1055-9965.epi-11-0421 |
| publishDate |
2011 |
| physical |
1263-1268 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Standard descriptive methods for the analysis of cancer surveillance data include canonical plots based on the lexis diagram, directly age-standardized rates (ASR), estimated annual percentage change (EAPC), and joinpoint regression. The age-period-cohort (APC) model has been used less often. Here, we argue that it merits much broader use. First, we describe close connections between estimable functions of the model parameters and standard quantities such as the ASR, EAPC, and joinpoints. Estimable functions have the added value of being fully adjusted for period and cohort effects, and generally more precise. Second, the APC model provides the descriptive epidemiologist with powerful new tools, including rigorous statistical methods for comparative analyses, and the ability to project the future burden of cancer. We illustrate these principles by using invasive female breast cancer incidence in the United States, but these concepts apply equally well to other cancer sites for incidence or mortality. Cancer Epidemiol Biomarkers Prev; 20(7); 1263–8. ©2011 AACR.</jats:p> |
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| author | Rosenberg, Philip S., Anderson, William F. |
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| description | <jats:title>Abstract</jats:title> <jats:p>Standard descriptive methods for the analysis of cancer surveillance data include canonical plots based on the lexis diagram, directly age-standardized rates (ASR), estimated annual percentage change (EAPC), and joinpoint regression. The age-period-cohort (APC) model has been used less often. Here, we argue that it merits much broader use. First, we describe close connections between estimable functions of the model parameters and standard quantities such as the ASR, EAPC, and joinpoints. Estimable functions have the added value of being fully adjusted for period and cohort effects, and generally more precise. Second, the APC model provides the descriptive epidemiologist with powerful new tools, including rigorous statistical methods for comparative analyses, and the ability to project the future burden of cancer. We illustrate these principles by using invasive female breast cancer incidence in the United States, but these concepts apply equally well to other cancer sites for incidence or mortality. Cancer Epidemiol Biomarkers Prev; 20(7); 1263–8. ©2011 AACR.</jats:p> |
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| imprint_str_mv | American Association for Cancer Research (AACR), 2011 |
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| spelling | Rosenberg, Philip S. Anderson, William F. 1055-9965 1538-7755 American Association for Cancer Research (AACR) Oncology Epidemiology http://dx.doi.org/10.1158/1055-9965.epi-11-0421 <jats:title>Abstract</jats:title> <jats:p>Standard descriptive methods for the analysis of cancer surveillance data include canonical plots based on the lexis diagram, directly age-standardized rates (ASR), estimated annual percentage change (EAPC), and joinpoint regression. The age-period-cohort (APC) model has been used less often. Here, we argue that it merits much broader use. First, we describe close connections between estimable functions of the model parameters and standard quantities such as the ASR, EAPC, and joinpoints. Estimable functions have the added value of being fully adjusted for period and cohort effects, and generally more precise. Second, the APC model provides the descriptive epidemiologist with powerful new tools, including rigorous statistical methods for comparative analyses, and the ability to project the future burden of cancer. We illustrate these principles by using invasive female breast cancer incidence in the United States, but these concepts apply equally well to other cancer sites for incidence or mortality. Cancer Epidemiol Biomarkers Prev; 20(7); 1263–8. ©2011 AACR.</jats:p> Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? Cancer Epidemiology, Biomarkers & Prevention |
| spellingShingle | Rosenberg, Philip S., Anderson, William F., Cancer Epidemiology, Biomarkers & Prevention, Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time?, Oncology, Epidemiology |
| title | Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_full | Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_fullStr | Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_full_unstemmed | Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_short | Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| title_sort | age-period-cohort models in cancer surveillance research: ready for prime time? |
| title_unstemmed | Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time? |
| topic | Oncology, Epidemiology |
| url | http://dx.doi.org/10.1158/1055-9965.epi-11-0421 |