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@ARTICLE{Knoll:164057,
      author       = {M. Knoll$^*$ and J. Furkel$^*$ and J. Debus$^*$ and A.
                      Abdollahi$^*$ and A. Karch and C. Stock$^*$},
      title        = {{A}n {R} package for an integrated evaluation of
                      statistical approaches to cancer incidence projection.},
      journal      = {BMC medical research methodology},
      volume       = {20},
      number       = {1},
      issn         = {1471-2288},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2020-02225},
      pages        = {257},
      year         = {2020},
      note         = {#EA:E050#LA:C070#},
      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.},
      cin          = {E050 / E210 / HD01 / C070},
      ddc          = {610},
      cid          = {I:(DE-He78)E050-20160331 / I:(DE-He78)E210-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)C070-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33059585},
      doi          = {10.1186/s12874-020-01133-5},
      url          = {https://inrepo02.dkfz.de/record/164057},
}