*  Clinical decision analysis: Incorporating the evidence with patient pr | PPA
Clinical decision analysis: Incorporating the evidence with patient preferences Ilyas S Aleem1, Hamza Jalal2, Idris S Aleem3, Adeel A Sheikh1, Mohit Bhandari11Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada; 2The University of Calgary, Calgary, Alberta, Canada; 3Carleton University, Ottawa, Ontario, CanadaAbstract: Decision analysis has become an increasingly popular decision-making tool with a multitude of clinical applications. Incorporating patient and expert preferences with available literature, it allows users to apply evidence-based medicine to make informed decisions when confronted with difficult clinical scenarios. A decision tree depicts potential alternatives and outcomes involved with a given decision. Probabilities and utilities are used to quantify the various options and help determine the best course of action. Sensitivity analysis allows ...
*  Clinical Xpert PDA Medical Reference and Decision-Support Tool
... ,The Thomson Clinical Xpert for PDAs is a new medical reference and decision-support tool. The Thomson Clinical Xpert puts authoritative drug, disease and laboratory information instantly into the hands of physicians and other prescribers, nurses, pharmacists, and clinical trainees, helping th,medicine,medical supply,medical supplies,medical product
*  Business Decision Analysis: An Active Learning Approach | Business & Management Special Topics | Business & Management |...
Part I: An Introduction to Business Decision Analysis:. 1. What is Business Decision Analysis?.. 2. Model-Building in Business Decision Analysis.. 3. The Components of a Mathematical Model.. 4. Deterministic and Stochastic Models.. 5. Single-attribute and Multi-attribute Problems.. 6. Sensitivity Analysis and Model Building.. Part II: Decision Analysis:.. 7. Decision Trees and Payoff Matrices.. 8. Decision-Making under Conditions of Uncertainty.. 9. Decision-Making under Conditions of Risk.. 10. Multi-Stage Decision Problems.. 11. Revising Probabilities.. 12. Extensions.. Part III: Linear Programming:.. 13. Formulating a Linear Programming Problem.. 14. Solving Linear Programming Problems Using a Graphical Method.. 15. Sensitivity Analysis of Solutions.. 16. Computer Solution of Linear Programming Problems.. 17. The Transportation Problem.. 18. The Assignment Problem.. 19. ...
*  UCSF Prostate Cancer Trial: Decision Support Tools for Men With Prostate Cancer- Clinical & Lifestyle Model
A UCSF PI-initiated study which includes comprehensive decision support intervention incorporating clinical, lifestyle, tumor genomic, and germline gene variant data.. The web and coaching intervention will: 1) summarize key clinical, lifestyle, and biomarker data elements, 2) communicate relative and absolute risks of upgrading/upstaging based on each of these elements, individually and in aggregate, and 3) provide tailored educational information for informed decision making on treatment options . A key aspect of the intervention will be provision of tiered coaching to the men prior to their physician visits to help them enter information accurately into the system, understand the results of the prediction model, document their questions for their physicians, and prepare them to make better-informed treatment decisions. UCSF research team will develop the decision support intervention in phases, initially ...
*  HE203: Techniques of Planning and Management Decision Analysis - Institute of Health Economics
Linear Programming: Decision problems. Formulating a decision problem into a linear programme. Solving Linear programming graphically. Infeasibility, unboundedness and redundant constraints.. Duality and Sensitivity Analysis in Linear Programming: Duality. Solving the dual using the solutions of the primal. Sensitivity analysis.. -Network Analysis: Network optimization problems. Representing the problem as a network. Formulating the problem as a linear programming. The maximum-flow problem. Fictitious nodes: solving transportation problems. Maximin objective function.. -Integer Programming and Goal Programming: Formulating an Integer linear programming (ILP). Solving an ILP. Goal programming: Target values and penalties. Formulating the goal programming.. Single Stage Decision Problems: Structuring decision problems. Solving decision problems. Taking account of attitude to risk. Some problems with expected utility theory.. ...
*  Decision analysis - Wikipedia
Decision analysis (DA) is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision, for prescribing a recommended course of action by applying the maximum expected utility action axiom to a well-formed representation of the decision, and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker and other stakeholders. Graphical representation of decision analysis problems commonly use framing tools, influence diagrams and decision trees. Such tools are used to represent the alternatives available to the decision maker, the ...
*  A quantitative approach to benefit-risk assessment of medicines - part 1: the development of a new model using multi-criteria...
One of the most important uses of benefit-risk assessment pertains to approval of new medicines by regulatory authorities and the subsequent review of these products during their life-cycle when new safety and/or efficacy data becomes available. At present, there exist no validated, well-accepted models for benefit-risk assessment that have the appropriate degree of sophistication, and as a consequence no models are widely used by regulatory authorities or industry. The aim of the study was therefore to develop a new model for benefit-risk assessment of medicines using multi-criteria decision analysis (MCDA). ...
*  Software packages for multi-criteria resource allocation - LSE Research Online
In this paper four commercial software packages for multi-criteria resource allocation are analyzed: Equity, HiPriority, Logical Decisions Portfolio and Expert Choice Resource Aligner. The key technical distinction concerns the type of resource allocation procedure used: Equity uses the benefit-to-cost ratio approach, HiPriority also uses the benefit-to-cost ratio approach and an exhaustive enumeration approach, whereas Logical Decisions Portfolio and Expert Choice Resource Aligner use a mathematical programming approach.. ...
*  February 2011 Phase 2 Funding Decision - Board Decisions - Board - The Global Fund to Fight AIDS, Tuberculosis and Malaria
Approves additional funding for the Phase 2 period for the proposals listed in Table 1 in the amounts indicated for each proposal, with the clear understanding that the amounts approved are upper ceilings rather than final funding amounts, and based on an understanding that the Secretariat shall pursue on-going implementation arrangements for each proposal consistent with the recommendations set forth in its Grant Score Card. Approval is also subject to paragraph 3.c. of the Comprehensive Funding Policy (GF/B20/DP9)[1] ...
*  Tutorial Lectures - The Second International Conferenceon Information Technology and Quantitative Management (ITQM 2014) -...
Multi-Criteria Decision Analysis (MCDA) is the area of the Decision Sciences that focus on structuring, analyzing, modeling, solving, and recommending a solution to decision problems in the presence of multiple criteria. One important issue in MCDA has to do with Robustness Analysis in discrete MCDA. Although there is not a single definition accepted by the scientific community, we can conveniently refer to robustness as the ability of a solution to cope with uncertainties. Different sources of uncertainty interact in a decision problem, some reflecting more or less arbitrary choices of the decision analyst and others concerning external uncertainties. In this sense, it has been proposed that the robustness concern needs to be explicit in a problem such that the robustness analysis is driven by a specific aim. A new robustness analysis framework is proposed where robustness of a solution in a decision aiding ...
*  A to Z Summary Results - Patient Decision Aids - Ottawa Hospital Research Institute
The decision aid helps patients clarify their values for outcomes of options by: a) asking people to think about which positive and negative features of the options matter most to them AND/OR b) describing each option to help patients imagine the physical, social, and /or psychological effect ...
*  2012 | Health Services Research Unit | The University of Aberdeen
Gillies, K., Skea, Z., Politi, M., and Brehaut, J. Decision support interventions for people making decisions about participation in clinical trials (Protocol). Cochrance Database of Systematic Reviews 2012, Art. No.: CD009736. DOI: 10.1002/14651858.CD009736.. Koretz, R. L, Avenell, A., and Lipman, T. O. Nutritional support for liver disease. Cochrane Database of Systematic Reviews 2012, Issue 5 Art. No.: CD008344.. ...
*  What was your BATNA (Best Alternative to a Negotiated Agreement)?: An Explanation of the Decision-Analytic Approach
We have each had to negotiate during the process of purchasing a home or automobile. Analyze the last major purchase that you were involved with and discuss that purchase in terms of the decision-analytic approach. What was your.
*  Approximations, simulation, and accuracy of multivariate discrete probability distributions in decision analysis
Many important decisions must be made without full information. For example, a woman may need to make a treatment decision regarding breast cancer without full knowledge of important uncertainties, such as how well she might respond to treatment. In the financial domain, in the wake of the housing crisis, the government may need to monitor the credit market and decide whether to intervene. A key input in this case would be a model to describe the chance that one person (or company) will default given that others have defaulted. However, such a model requires addressing the lack of knowledge regarding the correlation between groups or individuals. How to model and make decisions in cases where only partial information is available is a significant challenge. In the past, researchers have made arbitrary assumptions regarding the missing information. In this research, we developed a modeling procedure that can be used to analyze many possible scenarios subject ...
*  Clinical Prediction Rules: Cervical Spine | Tim Reynolds, PT, DPT, OCS, CSCS
Your patient walks into your clinic with neck pain that radiates into their arm...now what? How do you know what it is, what you should do, and if they are going to respond well to conservative therapy? With the help of several research studies we thankfully have the answer to some of these questions. Wainner…
*  WHO publishes list of bacteria for which new antibiotics ...
The list was developed in collaboration with the Division of Infectious Diseases at the University of Tübingen, Germany, using a multi-criteria decision analysis technique vetted by a group of international experts. The criteria for selecting pathogens on the list were: how deadly the infections they cause are; whether their treatment requires long hospital stays; how frequently they are resistant to existing antibiotics when people in communities catch them; how easily they spread between animals, from animals to humans, and from person to person; whether they can be prevented (e.g. through good hygiene and vaccination); how many treatment options remain; and whether new antibiotics to treat them are already in the R&D pipeline ...
*  EU Projects: EUR.nl
Over 50 million people in Europe have more than one chronic disease. This number will increase dramatically in the near future. This will increase health care spending to a staggering 20% of GDP. Multi-morbidity becomes the number one threat to population health and economic sustainability of health care systems. New models of care for multi-morbid patients are urgently needed. Given the diversity of Europe's health and social care systems there is no single model that fits them all. SELFIE aims to improve patient-centred care for patients with multi-morbidity by proposing evidence-based, economically sustainable integrated chronic care (ICC) models that stimulate cooperation across health and social care sectors and are supported by appropriate financing/payment schemes. SELFIE specifically focuses on multi-morbidity, on generating empirical evidence of the impact of ICC and on financing/payment schemes. It is methodologically innovative by applying Multi-Criteria Decision ...
*  Childhood CT scanning linked with increased cancer incidence | Health Imaging
A population-based cohort study revealed cancer incidence was 24 percent greater in people who underwent CT imaging at least one year prior to diagnosis, according to research published online May 22 in BMJ. The researchers called for increased awareness of the risks and benefits of CT imaging among providers and increased utilization of decision tools.
*  EMRA Clinical Prediction Card
The tri-fold Clinical Prediction Card is a great reminder of several commonly-used prediction rules for the emergency department.
*  Decision Aids - Rachel Thompson
This decision aid was developed by Aleena Wojcieszek, a Psychology Honours student I supervised in 2009. In an alternate allocation controlled trial, we found that exposure to this tool increased young people's knowledge of fertility decline over the lifespan and of the effectiveness of in vitro fertilisation. Exposure also decreased people's desired age at commencement and completion of childbearing. This trial was published here. ...
*  LOGSAM - Logistics Support Analysis Model | AcronymFinder
How is Logistics Support Analysis Model abbreviated? LOGSAM stands for Logistics Support Analysis Model. LOGSAM is defined as Logistics Support Analysis Model very rarely.
*  Halting problem
The halting problem is a decision problem about properties of computer programs on a fixed Turing-complete model of computation. The problem is to determine, given a program and an input to the program, whether the program will eventually halt when run with that input. In this abstract framework, there are no resource limitations of memory or time on the program's execution; it can take arbitrarily long, and use arbitrarily much storage space, before halting. The question is simply whether the given program will ever halt on a particular input.. For example, in pseudocode, the program. does not halt; rather, it goes on forever in an infinite loop. On the other hand, the program. halts very soon.. The halting problem is famous because it was one of the first problems proven algorithmically undecidable. This means there is no algorithm which can be applied to any arbitrary program and input to decide whether the program stops when run with that input.. Full article ▸. ...
*  10 design elements to consider before building an analytical model | SAS
When building a new analytical model, rushing headlong into construction is not a good idea. Careful planning and design will result in analytics you can trust.
*  10 design elements to consider before building an analytical model | SAS
When building a new analytical model, rushing headlong into construction is not a good idea. Careful planning and design will result in analytics you can trust.
*  Are the Three Treatment Means Equal?
Given the following sample information, test the hypothesis that the treatment means are equal at the .05 significance level. a. State the null hypothesis and the alternative hypothesis. b. What is the decision rule? c. Compute.