Analysing population-based cancer survival - settling the controversies
BMC Cancer, ISSN: 1471-2407, Vol: 16, Issue: 1, Page: 933
2016
- 81Citations
- 67Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations81
- Citation Indexes77
- 77
- CrossRef17
- Policy Citations4
- Policy Citation4
- Captures67
- Readers67
- 67
Article Description
Background: The relative survival field has seen a lot of development in the last decade, resulting in many different and even opposing suggestions on how to approach the analysis. Methods: We carefully define and explain the differences between the various measures of survival (overall survival, crude mortality, net survival and relative survival ratio) and study their differences using colon and prostate cancer data extracted from the national population-based cancer registry of Slovenia as well as simulated data. Results: The colon and prostate cancer data demonstrate clearly that when analysing population-based data, it is useful to split the overall mortality in crude probabilities of dying from cancer and from other causes. Complemented by net survival, it provides a complete picture of cancer survival in a given population. But when comparisons of different populations as defined for example by place or time are of interest, our simulated data demonstrate that net survival is the only measure to be used. Conclusions: The choice of the method should be done in two steps: first, one should determine the measure of interest and second, one should choose among the methods that estimate that measure consistently.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85001055566&origin=inward; http://dx.doi.org/10.1186/s12885-016-2967-9; http://www.ncbi.nlm.nih.gov/pubmed/27912732; http://bmccancer.biomedcentral.com/articles/10.1186/s12885-016-2967-9; https://dx.doi.org/10.1186/s12885-016-2967-9; https://bmccancer.biomedcentral.com/articles/10.1186/s12885-016-2967-9
Springer Science and Business Media LLC
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