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Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. WHO Collaborating Centre for International Drug Monitoring, Sweden.
Stockholm University, Faculty of Science, Department of Mathematics. WHO Collaborating Centre for International Drug Monitoring, Sweden.
2010 (English)In: Statistical Analysis and Data Mining, ISSN 1932-1864, E-ISSN 1932-1872, Vol. 3, no 4, 197-208 p.Article in journal (Refereed) Published
Abstract [en]

Most measures of interestingness for patterns of co-occurring events are based on data projections onto contingency tables for the events of primary interest. As an alternative, this article presents the first implementation of shrinkage logistic regression for large-scale pattern discovery, with an evaluation of its usefulness in real-world binary transaction data. Regression accounts for the impact of other covariates that may confound or otherwise distort associations. The application considered is international adverse drug reaction (ADR) surveillance, in which large collections of reports on suspected ADRs are screened for interesting reporting patterns worthy of clinical follow-up. Our results show that regression-based pattern discovery does offer practical advantages. Specifically it can eliminate false positives and false negatives due to other covariates. Furthermore, it identifies some established drug safety issues earlier than a measure based on contingency tables. While regression offers clear conceptual advantages, our results suggest that methods based on contingency tables will continue to play a key role in ADR surveillance, for two reasons: the failure of regression to identify some established drug safety concerns as early as the currently used measures, and the relative lack of transparency of the procedure to estimate the regression coefficients. This suggests shrinkage regression should be used in parallel to existing measures of interestingness in ADR surveillance and other large-scale pattern discovery applications.

Place, publisher, year, edition, pages
2010. Vol. 3, no 4, 197-208 p.
Keyword [en]
shrinkage regression, lasso, confounding, masking, direct and indirect associations, adverse drug reaction surveillance, drug safety, pharmacovigilance
National Category
Information Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-51946DOI: 10.1002/sam.10078OAI: oai:DiVA.org:su-51946DiVA: diva2:386428
Available from: 2011-01-12 Created: 2011-01-12 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Quantitative methods to support drug benefit-risk assessment
Open this publication in new window or tab >>Quantitative methods to support drug benefit-risk assessment
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Joint evaluation of drugs’ beneficial and adverse effects is required in many situations, in particular to inform decisions on initial or sustained marketing of drugs, or to guide the treatment of individual patients. This synthesis, known as benefit-risk assessment, is without doubt important: timely decisions supported by transparent and sound assessments can reduce mortality and morbidity in potentially large groups of patients. At the same time, it can be hugely complex: drug effects are generally disparate in nature and likelihood, and the information that needs to be processed is diverse, uncertain, deficient, or even unavailable. Hence there is a clear need for methods that can reliably and efficiently support the benefit-risk assessment process. For already marketed drugs, this process often starts with the detection of previously unknown risks that are subsequently integrated with all other relevant information for joint analysis.

In this thesis, quantitative methods are devised to support different aspects of drug benefit-risk assessment, and the practical usefulness of these methods is demonstrated in clinically relevant case studies. Shrinkage regression is adapted and implemented for large-scale screening in collections of individual case reports, leading to the discovery of a link between methylprednisolone and hepatotoxicity. This adverse effect is then considered as part of a complete benefit-risk assessment of methylpredniso­lone in multiple sclerosis relapses, set in a general framework of probabilistic decision analysis. Two methods devised in the thesis substantively contribute to this assessment: one for efficient generation of utility distributions for the considered clinical outcomes, driven by modelling of qualitative information; and one for computing risk limits for rare and otherwise non-quantifiable adverse effects, based on collections of individual case reports.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. 94 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-001
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-100286 (URN)978-91-7447-856-3 (ISBN)
Public defence
2014-03-21, Sal C, Forum 100, Isafjordsgatan 39, Kista, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defence the following papers were unpublished and had a status as follows: Paper 6: Manuscript; Paper 7: Manuscript.

Available from: 2014-02-27 Created: 2014-01-31 Last updated: 2015-11-16Bibliographically approved

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