Change search
ReferencesLink to record
Permanent link

Direct link
Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery
Stockholm University, Faculty of Science, Department of Mathematics.
2013 (English)In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 22, no 1, 57-69 p.Article in journal (Refereed) Published
Abstract [en]

Large observational data sets are a great asset to better understand the effects of medicines in clinical practice and, ultimately, improve patient care. For an empirical pattern in observational data to be of practical relevance, it should represent a substantial deviation from the null model. For the purpose of identifying such deviations, statistical significance tests are inadequate, as they do not on their own distinguish the magnitude of an effect from its data support. The observed-to-expected (OE) ratio on the other hand directly measures strength of association and is an intuitive basis to identify a range of patterns related to event rates, including pairwise associations, higher order interactions and temporal associations between events over time. It is sensitive to random fluctuations for rare events with low expected counts but statistical shrinkage can protect against spurious associations. Shrinkage OE ratios provide a simple but powerful framework for large-scale pattern discovery. In this article, we outline a range of patterns that are naturally viewed in terms of OE ratios and propose a straightforward and effective statistical shrinkage transformation that can be applied to any such ratio. The proposed approach retains emphasis on the practical relevance and transparency of highlighted patterns, while protecting against spurious associations.

Place, publisher, year, edition, pages
2013. Vol. 22, no 1, 57-69 p.
Keyword [en]
pattern discovery, statistical shrinkage, exploratory analysis, adverse drug reactions
National Category
Health Sciences Bioinformatics (Computational Biology) Probability Theory and Statistics
URN: urn:nbn:se:su:diva-90807DOI: 10.1177/0962280211403604ISI: 000317937900006OAI: diva2:627613


Available from: 2013-06-12 Created: 2013-06-11 Last updated: 2013-06-12Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Norén, G. Niklas
By organisation
Department of Mathematics
In the same journal
Statistical Methods in Medical Research
Health SciencesBioinformatics (Computational Biology)Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 63 hits
ReferencesLink to record
Permanent link

Direct link