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Crocetti, E and Randi, G (2016)

Using the Benford's Law as a first step to assess the quality of the cancer registry data

Frontiers in Public Health 4:225.

ISSN/ISBN: Not available at this time. DOI: 10.3389/fpubh.2016.00225



Abstract: Background: Benfordís law states that the distribution of the first digit different from 0 [first significant digit (FSD)] in many collections of numbers is not uniform. The aim of this study is to evaluate whether population-based cancer incidence rates follow Benfordís law, and if this can be used in their data quality check process. Methods: We sampled 43 population-based cancer registry populations (CRPs) from the Cancer Incidence in 5 Continents-volume X (CI5-X). The distribution of cancer incidence rate FSD was evaluated overall, by sex, and by CRP. Several statistics, including Pearsonís coefficient of correlation and distance measures, were applied to check the adherence to the Benfordís law. Results: In the whole dataset (146,590 incidence rates) and for each sex (70,722 male and 75,868 female incidence rates), the FSD distributions were Benford-like. The coefficient of correlation between observed and expected FSD distributions was extremely high (0.999), and the distance measures low. Considering single CRP (from 933 to 7,222 incidence rates), the results were in agreement with the Benfordís law, and only a few CRPs showed possible discrepancies from it. Conclusion: This study demonstrated for the first time that cancer incidence rates follow Benfordís law. This characteristic can be used as a new, simple, and objective tool in data quality evaluation. The analyzed data had been already checked for publication in CI5-X. Therefore, their quality was expected to be good. In fact, only for a few CRPs several statistics were consistent with possible violations.


Bibtex:
@article {, AUTHOR = {Crocetti, Emanuele and Randi, Giorgia }, TITLE = { Using the Benfordís Law as a First Step to Assess the Quality of the Cancer Registry Data }, JOURNAL = {Frontiers in Public Health}, YEAR = {2016}, VOLUME = {4:225}, NUMBER = {}, PAGES = {}, DOI = {10.3389/fpubh.2016.00225}, URL = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5061771/ }, }


Reference Type: Journal Article

Subject Area(s): General Interest