Quantitative methods to support drug benefit-risk assessment
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
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
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-100286ISBN: 978-91-7447-856-3OAI: oai:DiVA.org:su-100286DiVA: diva2:692438
2014-03-21, Sal C, Forum 100, Isafjordsgatan 39, Kista, 10:00 (English)
Rawlins, Sir Michael, Professor
Ekenberg, Love, ProfessorDanielson, Mats, ProfessorEdwards, I. Ralph, Professor
At the time of the doctoral defence the following papers were unpublished and had a status as follows: Paper 6: Manuscript; Paper 7: Manuscript.2014-02-272014-01-312015-11-16Bibliographically approved
List of papers