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      Quantitative benefit-risk assessment of methylprednisolone in multiple sclerosis relapses

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          Abstract

          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12883-015-0450-x) contains supplementary material, which is available to authorized users.

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          Most cited references41

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          VigiBase, the WHO Global ICSR Database System: Basic Facts

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            Decision Analysis: Practice and Promise

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              Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach.

              The data for medical decision analyses are often unreliable. Traditional sensitivity analysis--varying one or more probability or utility estimates from baseline values to see if the optimal strategy changes--is cumbersome if more than two values are allowed to vary concurrently. This paper describes a practical method for probabilistic sensitivity analysis, in which uncertainties in all values are considered simultaneously. The uncertainty in each probability and utility is assumed to possess a probability distribution. For ease of application we have used a parametric model that permits each distribution to be specified by two values: the baseline estimate and a bound (upper or lower) of the 95 percent confidence interval. Following multiple simulations of the decision tree in which each probability and utility is randomly assigned a value within its distribution, the following results are recorded: (a) the mean and standard deviation of the expected utility of each strategy; (b) the frequency with which each strategy is optimal; (c) the frequency with which each strategy "buys" or "costs" a specified amount of utility relative to the remaining strategies. As illustrated by an application to a previously published decision analysis, this technique is easy to use and can be a valuable addition to the armamentarium of the decision analyst.
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                Author and article information

                Contributors
                ola.caster@who-umc.org
                ralph.edwards@who-umc.org
                Journal
                BMC Neurol
                BMC Neurol
                BMC Neurology
                BioMed Central (London )
                1471-2377
                16 October 2015
                16 October 2015
                2015
                : 15
                : 206
                Affiliations
                [ ]Uppsala Monitoring Centre (UMC), Box 1051, SE-751 40 Uppsala, Sweden
                [ ]Department of Computer and Systems Sciences, Stockholm University, Postbox 7003, SE-164 07 Kista, Sweden
                Article
                450
                10.1186/s12883-015-0450-x
                4609048
                2fe57498-7438-490a-a14a-0f8277bafbb2
                © Caster and Edwards. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 27 March 2015
                : 29 September 2015
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Neurology
                glucocorticoids,corticosteroids,ms,neurology,neuropathy,demyelinating diseases,pharmacoepidemiology,pharmacovigilance,clinical epidemiology,decision analysis

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