* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Document
Survey
Document related concepts
Polysubstance dependence wikipedia , lookup
Plateau principle wikipedia , lookup
Drug design wikipedia , lookup
Neuropharmacology wikipedia , lookup
Drug discovery wikipedia , lookup
Pharmacogenomics wikipedia , lookup
Pharmacokinetics wikipedia , lookup
Prescription costs wikipedia , lookup
Pharmaceutical industry wikipedia , lookup
Drug interaction wikipedia , lookup
Prescription drug prices in the United States wikipedia , lookup
Psychopharmacology wikipedia , lookup
Pharmacognosy wikipedia , lookup
Transcript
A state of the art multi-criteria model for drug benefit-risk analysis T. Tervonen (1), D. Postmus (2), H.L. Hillege (3) (1) Faculty of Economics and Business, RUG.nl (2) Department of Epidemiology /+ (3) Cardiology, UMCG.nl What? Drug benefit-risk analysis is based on firm clinical evidence expressing various factors. We propose a supporting multi-criteria model that fully takes into account the clinical evidence and allows quantifying tradeoffs between drugs of the same therapeutic class. How? Stochastic Multicriteria Acceptability Analysis (SMAA) is used as the decision-aiding model in our study. SMAA allows computing the typical value judgments that support a decision, to quantify uncertainty, and to compute a comprehensive benefit-risk profile. We constructed a multi-criteria model for ranking drugs for depression with respect to different benefit and risk criteria. Methods. We analyzed Fluoxetine, Paroxetine, Sertraline, and Venlafaxine according to relative efficacy and absolute rates of most common adverse drug reactions using meta-analytical data from literature. We did three analyses: one without preference information and two with criteria rankings elicited from an expert in the field of antidepressants. We explained the SMAA model and multi-attribute utility theory to the expert and asked her to consider two scenarios: mild and severe depression. Our model showed that there are clear tradeoffs within and between the four drugs with respect to the approach without preference information (Figure 1), mild depression (Figure 2) and severe depression (Figure 3). Main results. 1. We separated clinical data from subjective judgments, thereby increasing the transparency of the decision making process. 2. In contrast to previous applications of multi-criteria methods, our approach is based on the SMAA methodology, that allows us to take into account the sampling variation that is inherent in outcome measurements in clinical trials and/or observational studies. 3. Analysis without preferences allows to quantify tradeoffs between drugs. 4. Scenario-based analysis incorporating preferences in the model can be used to improve risk assessment and management of new drugs. This study was performed in the context of the Escher project (T6-202), a project of the Dutch Top Institute Pharma