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Combining Second-Order Belief Distributions with Qualitative Statements in Decision Analysis
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Uppsala Monitoring Centre, Sweden.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2012 (English)In: Managing Safety of Heterogeneous Systems: Decisions under Uncertainties and Risks / [ed] Yuri Ermoliev, Marek Makowski, Kurt Marti, Springer Berlin/Heidelberg, 2012, 67-87 p.Chapter in book (Refereed)
Abstract [en]

There is often a need to allow for imprecise statements in real-world decision analysis. Joint modeling of intervals and qualitative statements as constraint sets is one important approach to solving this problem, with the advantage that both probabilities and utilities can be handled. However, a major limitation with interval-based approaches is that aggregated quantities such as expected utilities also become intervals, which often hinders efficient discrimination. The discriminative power can be increased by utilizing second-order information in the form of belief distributions, and this paper demonstrates how qualitative relations between variables can be incorporated into such a framework. The general case with arbitrary distributions is described first, and then a computationally efficient simulation algorithm is presented for a relevant sub-class of analyses. By allowing qualitative relations, our approach preserves the ability of interval-based methods to be deliberately imprecise. At the same time, the use of belief distributions allows more efficient discrimination, and it provides a semantically clear interpretation of the resulting beliefs within a probabilistic framework.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2012. 67-87 p.
Series
Lecture Notes in Economics and Mathematical Systems, ISSN 0075-8442 ; 658
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-75018DOI: 10.1007/978-3-642-22884-1_4ISBN: 978-3-642-22883-4 (print)ISBN: 978-3-642-22884-1 (print)OAI: oai:DiVA.org:su-75018DiVA: diva2:513661
Available from: 2012-04-03 Created: 2012-04-03 Last updated: 2015-11-16Bibliographically 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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