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A statistical methodology for drug–drug interaction surveillance
Stockholm University, Faculty of Science, Department of Mathematics.
Stockholm University, Faculty of Science, Department of Mathematics.
2008 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 27, no 16, 3057-3070 p.Article in journal (Refereed) Published
Abstract [en]

Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug–ADR associations, but have yet to reach their full potential for drug–drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug–drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug–drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug–drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug–drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.

Place, publisher, year, edition, pages
2008. Vol. 27, no 16, 3057-3070 p.
Keyword [en]
adverse drug reaction, exploratory analysis, interaction, shrinkage, surveillance
National Category
Mathematics
Identifiers
URN: urn:nbn:se:su:diva-14900DOI: 10.1002/sim.3247ISI: 000257567900005OAI: oai:DiVA.org:su-14900DiVA: diva2:181420
Available from: 2008-11-07 Created: 2008-11-07 Last updated: 2017-12-13Bibliographically approved
In thesis
1. Statistical methods for knowledge discovery in adverse drug reaction surveillance
Open this publication in new window or tab >>Statistical methods for knowledge discovery in adverse drug reaction surveillance
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Collections of individual case safety reports are the main resource for early discovery of unknown adverse reactions to drugs once they have been introduced to the general public. The data sets involved are complex and based on voluntary submission of reports, but contain pieces of very important information. The aim of this thesis is to propose computationally feasible statistical methods for large-scale knowledge discovery in these data sets. The main contributions are a duplicate detection method that can reliably identify pairs of unexpectedly similar reports and a new measure for highlighting suspected drug-drug interaction.

Specifically, we extend the hit-miss model for database record matching with a hit-miss mixture model for scoring numerical record fields and a new method to compensate for strong record field correlations. The extended hit-miss model is implemented for the WHO database and demonstrated to be useful in real world duplicate detection, despite the noisy and incomplete information on individual case safety reports. The Information Component measure of disproportionality has been in routine use since 1998 to screen the WHO database for excessive adverse drug reaction reporting rates. Here, it is further refined. We introduce improved credibility intervals for rare events, post-stratification adjustment for suspected confounders and an extension to higher order associations that allows for simple but robust screening for potential risk factors. A new approach to identifying reporting patterns indicative of drug-drug interaction is also proposed. Finally, we describe how imprecision estimates specific to each prediction of a Bayes classifier may be obtained with the Bayesian bootstrap. Such case-based imprecision estimates allow for better prediction when different types of errors have different associated loss, with a possible application in combining quantitative and clinical filters to highlight drug-ADR pairs for clinical review.

Place, publisher, year, edition, pages
Stockholm: Matematiska institutionen, 2007. 41 p.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-6764 (URN)91-7155-411-4 (ISBN)
Public defence
2007-05-07, sal 14, hus 5, Kräftriket, Stockholm, 13:00
Opponent
Supervisors
Available from: 2007-04-16 Created: 2007-04-10 Last updated: 2011-06-20Bibliographically approved

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