See the published version of this article at BMC Medical Research Methodology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
An R package for an integrated evaluation of statistical approaches to cancer incidence projection
Maximilian Knoll
Deutsches Krebsforschungszentrum
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9037-3980
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Medical Research Methodology.
Background: Projection of future cancer incidence is an important task in cancer epidemiology. The results are of interest also for biomedical research and public health policy. Age-Period-Cohort (APC) models, usually based on long-term cancer registry data (>20yrs), are established for such projections. In many countries (including Germany), however, nationwide long-term data are not yet available. General guidance on statistical approaches for projections using rather short-term data is challenging and software to enable researchers to easily compare approaches is lacking.
Methods: To enable a comparative analysis of the performance of statistical approaches to cancer incidence projection, we developed an R package (incAnalysis), supporting in particular Bayesian models fitted by Integrated Nested Laplace Approximations (INLA). Its use is demonstrated by an extensive empirical evaluation of operating characteristics (bias, coverage and precision) of potentially applicable models differing by complexity. Observed long-term data from three cancer registries (SEER-9, NORDCAN, Saarland) was used for benchmarking.
Results: Overall, coverage was high (mostly >90%) for Bayesian APC models (BAPC), whereas less complex models showed differences in coverage dependent on projection-period. Intercept-only models yielded values below 20% for coverage. Bias increased and precision decreased for longer projection periods (>15 years) for all except intercept-only models. Precision was lowest for complex models such as BAPC models, generalized additive models with multivariate smoothers and generalized linear models with age x period interaction effects.
Conclusion: The incAnalysis R package allows a straightforward comparison of cancer incidence rate projection approaches. Further detailed and targeted investigations into model performance in addition to the presented empirical results are recommended to derive guidance on appropriate statistical projection methods in a given setting.
Keywords
Cancer epidemiology, Age-Period-Cohort model, Bayesian model, Cancer incidence projection, INLA
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 3
Posted 23 Sep, 2020
No comments provided
Editorial decision: Minor revision
On 17 Sep, 2020
Review #1 received
Received 12 Sep, 2020
Reviewers invited
Invitations sent on 26 Aug, 2020
Reviewer #1 agreed
On 26 Aug, 2020
Editor assigned
On 19 Aug, 2020
Submission checks complete
On 18 Aug, 2020
Editor invited
On 18 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 07 Aug, 2020
Review #2 received
Received 05 Aug, 2020
Reviewer #2 agreed
On 28 Jul, 2020
Review #1 received
Received 21 Jul, 2020
Reviewer #1 agreed
On 21 Jun, 2020
Reviewers invited
Invitations sent on 20 Jun, 2020
Editor invited
On 10 Jun, 2020
Editor assigned
On 09 Jun, 2020
Submission checks complete
On 08 Jun, 2020
First submitted
On 07 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Statistical Epidemiology
Learn more about our company.
This preprint is under consideration at BMC Medical Research Methodology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
An R package for an integrated evaluation of statistical approaches to cancer incidence projection
Maximilian Knoll
Deutsches Krebsforschungszentrum
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9037-3980
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 3
Posted 23 Sep, 2020
No comments provided
Editorial decision: Minor revision
On 17 Sep, 2020
Review #1 received
Received 12 Sep, 2020
Reviewers invited
Invitations sent on 26 Aug, 2020
Reviewer #1 agreed
On 26 Aug, 2020
Editor assigned
On 19 Aug, 2020
Submission checks complete
On 18 Aug, 2020
Editor invited
On 18 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 07 Aug, 2020
Review #2 received
Received 05 Aug, 2020
Reviewer #2 agreed
On 28 Jul, 2020
Review #1 received
Received 21 Jul, 2020
Reviewer #1 agreed
On 21 Jun, 2020
Reviewers invited
Invitations sent on 20 Jun, 2020
Editor invited
On 10 Jun, 2020
Editor assigned
On 09 Jun, 2020
Submission checks complete
On 08 Jun, 2020
First submitted
On 07 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Statistical Epidemiology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Medical Research Methodology.
Background: Projection of future cancer incidence is an important task in cancer epidemiology. The results are of interest also for biomedical research and public health policy. Age-Period-Cohort (APC) models, usually based on long-term cancer registry data (>20yrs), are established for such projections. In many countries (including Germany), however, nationwide long-term data are not yet available. General guidance on statistical approaches for projections using rather short-term data is challenging and software to enable researchers to easily compare approaches is lacking.
Methods: To enable a comparative analysis of the performance of statistical approaches to cancer incidence projection, we developed an R package (incAnalysis), supporting in particular Bayesian models fitted by Integrated Nested Laplace Approximations (INLA). Its use is demonstrated by an extensive empirical evaluation of operating characteristics (bias, coverage and precision) of potentially applicable models differing by complexity. Observed long-term data from three cancer registries (SEER-9, NORDCAN, Saarland) was used for benchmarking.
Results: Overall, coverage was high (mostly >90%) for Bayesian APC models (BAPC), whereas less complex models showed differences in coverage dependent on projection-period. Intercept-only models yielded values below 20% for coverage. Bias increased and precision decreased for longer projection periods (>15 years) for all except intercept-only models. Precision was lowest for complex models such as BAPC models, generalized additive models with multivariate smoothers and generalized linear models with age x period interaction effects.
Conclusion: The incAnalysis R package allows a straightforward comparison of cancer incidence rate projection approaches. Further detailed and targeted investigations into model performance in addition to the presented empirical results are recommended to derive guidance on appropriate statistical projection methods in a given setting.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.