TY  - JOUR
AU  - Knoll, Maximilian
AU  - Furkel, Jennifer
AU  - Debus, Jürgen
AU  - Abdollahi, Amir
AU  - Karch, André
AU  - Stock, Christian
TI  - An R package for an integrated evaluation of statistical approaches to cancer incidence projection.
JO  - BMC medical research methodology
VL  - 20
IS  - 1
SN  - 1471-2288
CY  - Heidelberg
PB  - Springer
M1  - DKFZ-2020-02225
SP  - 257
PY  - 2020
N1  - #EA:E050#LA:C070#
AB  - 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
LB  - PUB:(DE-HGF)16
C6  - pmid:33059585
DO  - DOI:10.1186/s12874-020-01133-5
UR  - https://inrepo02.dkfz.de/record/164057
ER  -