Article
periodR – an R package to calculate long term cancer survival estimates using period analysis
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Authors
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Bernd Holleczek
- Saarland Cancer Registry, Ministry for Justice, Health and Social Services, Saarbrücken
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Adam Gondos
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg
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Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg
| Published: | September 6, 2007 |
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Outline
Text
Introduction: Long-term survival estimates are key outcome measures reported by cancer registries. Period analysis provides up-to-date estimates of long term survival [Ref. 1], [Ref. 2]. It enables early detection of changes in survival trends and better predicts long term survival of recently
diagnosed patients compared to survival estimates derived by traditional cohort-based methods [Ref. 3], [Ref. 4], [Ref. 5]. We present a software package for the R language and environment for statistical computing to perform period analysis.
Methods: The package implements proposed computer programs [Ref. 6] and uses a life-table approach in which conditional survival probabilities for 1-year intervals following diagnosis are combined to derive cumulative absolute and relative survival proportions which exclusively reflect survival experience of patients within some most recent calendar period for which cancer incidence and mortality follow-up data is available.
Besides period analysis, the software can also be used to perform other analyses like cohort, complete or hybrid analysis [Ref. 7]. Modified versions of the analysis procedures to compute age adjusted survival estimates are also included [Ref. 8].
The software requires a dataset which must contain the following variables: month and year of diagnosis, month and year of end of follow-up, sex, age at diagnosis and vital status at end of follow-up. Conditional survival probabilities for the general population similar to the patient group with respect to sex and age are needed for the estimation of relative survival.
Application: R is available as free software and runs on many platforms [Ref. 9]. In addition the package includes sample data, an example how to obtain conditional survival probabilities from publicly available life-tables as well as integrated documentation with examples of different analyses and graphical output.
Conclusions: The package is free software and is planned to be released shortly. The software will be available at the website of the Saarland Cancer Registry (http://www.krebsregister.saarland.de/improve). We hope that it will find a wide audience and enhance feasibility and application of period analysis of absolute and relative survival in the cancer registry setting.
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