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Quantitative Benefit-Risk Assessment Using Only Qualitative Information on Utilities
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Uppsala Monitoring Centre, Sweden.
Stockholm University, Faculty of Science, Department of Mathematics. Uppsala Monitoring Centre, Sweden.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2012 (English)In: Medical decision making, ISSN 0272-989X, E-ISSN 1552-681X, Vol. 32, no 6, E1-E15 p.Article in journal (Refereed) Published
Abstract [en]

Background: Utilities of pertinent clinical outcomes are crucial variables for assessing the benefits and risks of drugs, but numerical data on utilities may be unreliable or altogether missing. We propose a method to incorporate qualitative information into a probabilistic decision analysis framework for quantitative benefit-risk assessment. Objective: To investigate whether conclusive results can be obtained when the only source of discriminating information on utilities is widely agreed upon qualitative relations, for example, ''sepsis is worse than transient headache'' or ''alleviation of disease is better without than with complications.'' Method: We used the structure and probabilities of 3 published models that were originally evaluated based on the standard metric of quality-adjusted life years (QALYs): terfenadine versus chlorpheniramine for the treatment of allergic rhinitis, MCV4 vaccination against meningococcal disease, and alosetron for irritable bowel syndrome. For each model, we identified clinically straightforward qualitative relations among the outcomes. Using Monte Carlo simulations, the resulting utility distributions were then combined with the previously specified probabilities, and the rate of preference in terms of expected utility was determined for each alternative. Results: Our approach conclusively favored MCV4 vaccination, and it was concordant with the QALY assessments for the MCV4 and terfenadine versus chlorpheniramine case studies. For alosetron, we found a possible unfavorable benefit-risk balance for highly risk-averse patients not identified in the original analysis. Conclusion: Incorporation of widely agreed upon qualitative information into quantitative benefit-risk assessment can provide for conclusive results. The methods presented should prove useful in both population and individual-level assessments, especially when numerical utility data are missing or unreliable, and constraints on time or money preclude its collection.

Place, publisher, year, edition, pages
2012. Vol. 32, no 6, E1-E15 p.
Keyword [en]
benefit-risk, risk-benefit, comparative effectiveness, probabilistic, simulation, Monte Carlo, decision analysis, decision-analytical, quality-adjusted life years, utility modeling, alosetron, MCV4 vaccination, terfenadine, chlorpheniramine
National Category
Health Sciences Mathematics Computer and Information Science
URN: urn:nbn:se:su:diva-84776DOI: 10.1177/0272989X12451338ISI: 000311802700001OAI: diva2:582017


Available from: 2013-01-03 Created: 2013-01-02 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.
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-001
National Category
Information Systems
Research subject
Computer and Systems Sciences
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)

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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Caster, OlaNorén, G. NiklasEkenberg, Love
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