A state-of-the-art multi-criteria model for drug benefit-risk analysis

Drug benefit-risk analysis is based on firm clinical evidence related to various safety and efficacy outcomes, such as tolerability, treatment response, and adverse events. In this paper, we propose a new approach for constructing a supporting multi-criteria model that fully takes into account this evidence. Our approach is based on the Stochastic Multicriteria Acceptability Analysis (SMAA) methodology, which allows us to compute the typical value judgments that support a decision, to quantify uncertainty, and to compute a comprehensive benefit-risk profile. As an example, we constructed a multi-criteria model for the therapeutic group of second-generation antidepressants. We analyzed Fluoxetine, Paroxetine, Sertraline, and Venlafaxine according to relative efficacy and absolute rates of several common adverse drug reactions using meta-analytical data from the literature. Our model showed that there are clear trade-offs among the four drugs. Based on our experiences from this study, SMAA appears to be a suitable approach for quantifying trade-offs and decision uncertainty in drug benefit-risk analysis.

Asynchronous agent-based simulation and optimization of parallel business

A Port Container Terminal (PCT) involves complex business processes which are carried out by at least four organizations, namely PCT Operator, Customer, Quarantine and Customs. Each organization produces event log data from the activities. The event log data from the four organizations contain synchronous and asynchronous activities. In this research, the four organizations are represented by four agents. By simulating this log data using agent based simulation, we get the performance of the current business process. The performance indicators gathered are time and cost which are needed to do the activity (task). After the simulation is complete, we found Asynchronous Waiting Time (AWT). AWT is waiting time which happens because the agent in the simulation cannot do the newly assigned task because the agent is still working on the other task. Therefore, we parallelize the task performed by the agent so that the agent can do multiple tasks at a time. After we parallelize the task, we perform an optimization process using Stochastic Multicriteria Adaptability Analysis 2 (SMAA-2). Thus, the optimal amount of task an agent can do simultaneously is analyzed. This study result shows that parallelization can reduce AWT of the current system and the optimization process using SMAA-2 shows the most optimal number of multiple tasks an agent can do simultaneously

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Decision modelling tools for utilities in the deregulated energy market

This thesis examines the impact of the deregulation of the energy market on decision making and optimisation in utilities and demonstrates how decision support applications can solve specific encountered tasks in this context. The themes of the thesis are presented in different frameworks in order to clarify the complex decision making and optimisation environment where new sources of uncertainties arise due to the convergence of energy markets, globalisation of energy business and increasing competition. This thesis reflects the changes in the decision making and planning environment of European energy companies during the period from 1995 to 2004. It also follows the development of computational performance and evolution of energy information systems during the same period. Specifically, this thesis consists of studies at several levels of the decision making hierarchy ranging from top-level strategic decision problems to specific optimisation algorithms. On the other hand, the studies also follow the progress of the liberalised energy market from the monopolistic era to the fully competitive market with new trading instruments and issues like emissions trading. This thesis suggests that there is an increasing need for optimisation and multiple criteria decision making methods, and that new approaches based on the use of operations research are welcome as the deregulation proceeds and uncertainties increase. Technically, the optimisation applications presented are based on Lagrangian relaxation techniques and the dedicated Power Simplex algorithm supplemented with stochastic scenario analysis for decision support, a heuristic method to allocate common benefits and potential losses of coalitions of power companies, and an advanced Branch-and-Bound algorithm to solve efficiently non-convex optimisation problems. The optimisation problems are part of the operational and tactical decision making process that has become very complex in the recent years. Similarly, strategic decision support has also faced new challenges. This thesis introduces two applications involving multiple criteria decision making methods. The first application explores the decision making problem caused by the introduction of 'green' electricity that creates additional value for renewable energy. In this problem the stochastic multi-criteria acceptability analysis method (SMAA) is applied. The second strategic multi-criteria decision making study discusses two different energy-related operations research problems: the elements of risk analysis in the energy field and the evaluation of different choices with a decision support tool accommodating incomplete preference information to help energy companies to select a proper risk management system. The application is based on the rank inclusion in criteria hierarchies (RICH) method.reviewe

Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts