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Quantitative methods to support drug benefit-risk assessment
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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: urn:nbn:se:su:diva-100286ISBN: 978-91-7447-856-3 (print)OAI: oai:DiVA.org:su-100286DiVA: diva2:692438
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
List of papers
1. Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database
Open this publication in new window or tab >>Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database
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.

Keyword
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:nbn:se:su:diva-51946 (URN)10.1002/sam.10078 (DOI)
Available from: 2011-01-12 Created: 2011-01-12 Last updated: 2017-12-11Bibliographically approved
2. Reflections on Attribution and Decisions in Pharmacovigilance
Open this publication in new window or tab >>Reflections on Attribution and Decisions in Pharmacovigilance
2010 (English)In: Drug Safety, ISSN 0114-5916, E-ISSN 1179-1942, Vol. 33, no 10, 805-809 p.Article in journal, Editorial material (Refereed) Published
National Category
Information Science
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-51980 (URN)10.2165/11532460-000000000-00000 (DOI)000282559300001 ()
Available from: 2011-01-12 Created: 2011-01-12 Last updated: 2017-12-11Bibliographically approved
3. Methylprednisolone-induced hepatotoxicity: experiences from global adverse drug reaction surveillance
Open this publication in new window or tab >>Methylprednisolone-induced hepatotoxicity: experiences from global adverse drug reaction surveillance
2014 (English)In: European Journal of Clinical Pharmacology, ISSN 0031-6970, E-ISSN 1432-1041, Vol. 70, no 4, 501-503 p.Article in journal, Letter (Refereed) Published
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:su:diva-100282 (URN)10.1007/s00228-013-1632-3 (DOI)000332961700017 ()
Available from: 2014-01-31 Created: 2014-01-31 Last updated: 2017-12-06Bibliographically approved
4. Combining Second-Order Belief Distributions with Qualitative Statements in Decision Analysis
Open this publication in new window or tab >>Combining Second-Order Belief Distributions with Qualitative Statements in Decision Analysis
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
Series
Lecture Notes in Economics and Mathematical Systems, ISSN 0075-8442 ; 658
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-75018 (URN)10.1007/978-3-642-22884-1_4 (DOI)978-3-642-22883-4 (ISBN)978-3-642-22884-1 (ISBN)
Available from: 2012-04-03 Created: 2012-04-03 Last updated: 2015-11-16Bibliographically approved
5. Quantitative Benefit-Risk Assessment Using Only Qualitative Information on Utilities
Open this publication in new window or tab >>Quantitative Benefit-Risk Assessment Using Only Qualitative Information on Utilities
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.

Keyword
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
Identifiers
urn:nbn:se:su:diva-84776 (URN)10.1177/0272989X12451338 (DOI)000311802700001 ()
Note

AuthorCount:4;

Available from: 2013-01-03 Created: 2013-01-02 Last updated: 2017-12-06Bibliographically approved
6. Computing limits on medicine risks based on collections of individual case reports
Open this publication in new window or tab >>Computing limits on medicine risks based on collections of individual case reports
2014 (English)In: Theoretical Biology Medical Modelling, ISSN 1742-4682, E-ISSN 1742-4682, Vol. 11, 15Article in journal (Refereed) Published
Abstract [en]

Background: Quantifying a medicine's risks for adverse effects is crucial in assessing its value as a therapeutic agent. Rare adverse effects are often not detected until after the medicine is marketed and used in large and heterogeneous patient populations, and risk quantification is even more difficult. While individual case reports of suspected harm from medicines are instrumental in the detection of previously unknown adverse effects, they are currently not used for risk quantification. The aim of this article is to demonstrate how and when limits on medicine risks can be computed from collections of individual case reports. Methods: We propose a model where drug exposures in the real world may be followed by adverse episodes, each containing one or several adverse effects. Any adverse episode can be reported at most once, and each report corresponds to a single adverse episode. Based on this model, we derive upper and lower limits for the per-exposure risk of an adverse effect for a given drug. Results: An upper limit for the per-exposure risk of the adverse effect Y for a given drug X is provided by the reporting ratio of X together with Y relative to all reports on X, under two assumptions: (i) the average number of adverse episodes following exposure to X is one or less; and (ii) adverse episodes that follow X and contain Y are more frequently reported than adverse episodes in general that follow X. Further, a lower risk limit is provided by dividing the number of reports on X together with Y by the total number of exposures to X, under the assumption that exposures to X that are followed by Y generate on average at most one report on X together with Y. Using real data, limits for the narcolepsy risk following Pandemrix vaccination and the risk of coeliac disease following antihypertensive treatment were computed and found to conform to reference risk values from epidemiological studies. Conclusions: Our framework enables quantification of medicine risks in situations where this is otherwise difficult or impossible. It has wide applicability, but should be particularly useful in structured benefit-risk assessments that include rare adverse effects.

Keyword
Pharmacovigilance, Pharmacoepidemiology, Benefit-risk, Risk-benefit, Adverse drug reactions, Adverse drug reaction surveillance, Post-marketing, Post-marketing surveillance, Spontaneous reports
National Category
Mathematics Bioinformatics (Computational Biology)
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-103987 (URN)10.1186/1742-4682-11-15 (DOI)000334626600002 ()
Note

AuthorCount:3;

Available from: 2014-06-02 Created: 2014-05-27 Last updated: 2017-12-05Bibliographically approved
7. Quantitative benefit-risk assessment of methylprednisolone in multiple sclerosis relapses
Open this publication in new window or tab >>Quantitative benefit-risk assessment of methylprednisolone in multiple sclerosis relapses
2015 (English)In: BMC Neurology, ISSN 1471-2377, E-ISSN 1471-2377, Vol. 15, 206Article in journal (Refereed) Published
Abstract [en]

Background: High-dose short-term methylprednisolone is the recommended treatment in the management of multiple sclerosis relapses, although it has been suggested that lower doses may be equally effective. Also, glucocorticoids are associated with multiple and often dose-dependent adverse effects. This quantitative benefit-risk assessment compares high-and low-dose methylprednisolone (at least 2000 mg and less than 1000 mg, respectively, during at most 31 days) and a no treatment alternative, with the aim of determining which regimen, if any, is preferable in multiple sclerosis relapses. Methods: An overall framework of probabilistic decision analysis was applied, combining data from different sources. Effectiveness as well as risk of non-serious adverse effects were estimated from published clinical trials. However, as these trials recorded very few serious adverse effects, risk intervals for the latter were derived from individual case reports together with a range of plausible distributions. Probabilistic modelling driven by logically implied or clinically well motivated qualitative relations was used to derive utility distributions. Results: Low-dose methylprednisolone was not a supported option in this assessment; there was, however, only limited data available for this treatment alternative. High-dose methylprednisolone and the no treatment alternative interchanged as most preferred, contingent on the risk distributions applied for serious adverse effects, the assumed level of risk aversiveness in the patient population, and the relapse severity. Conclusions: The data presently available do not support a change of current treatment recommendations. There are strong incentives for further clinical research to reduce the uncertainty surrounding the effectiveness and the risks associated with methylprednisolone in multiple sclerosis relapses; this would enable better informed and more precise treatment recommendations in the future.

Keyword
Glucocorticoids, Corticosteroids, MS, Neurology, Neuropathy, Demyelinating diseases, Pharmacoepidemiology, Pharmacovigilance, Clinical epidemiology, Decision analysis
National Category
Neurology
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-122922 (URN)10.1186/s12883-015-0450-x (DOI)000362870500001 ()
Available from: 2015-11-16 Created: 2015-11-11 Last updated: 2017-12-01Bibliographically approved

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