Journal Article DKFZ-2020-02225

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An R package for an integrated evaluation of statistical approaches to cancer incidence projection.

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2020
Springer Heidelberg

BMC medical research methodology 20(1), 257 () [10.1186/s12874-020-01133-5]
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Abstract: 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 (> 20 yrs), 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.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.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.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.

Classification:

Note: #EA:E050#LA:C070#

Contributing Institute(s):
  1. E050 KKE Strahlentherapie (E050)
  2. E210 KKE Translationale Radioonkologie (E210)
  3. DKTK HD zentral (HD01)
  4. C070 Klinische Epidemiologie und Alternf. (C070)
Research Program(s):
  1. 313 - Cancer risk factors and prevention (POF3-313) (POF3-313)

Appears in the scientific report 2020
Database coverage:
Medline ; Creative Commons Attribution CC BY (No Version) ; DOAJ ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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 Record created 2020-10-21, last modified 2024-02-29



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