Multi criteria decision analysis for healthcareSunhong Kwon
Multi-criteria decision analysis (MCDA) is a process used to evaluate multiple criteria in healthcare decision making. Three key aspects of MCDA are summarized:
1. MCDA identifies criteria like clinical benefits, safety, costs, and other factors to evaluate healthcare interventions. Studies consider criteria like disease severity, population affected, clinical guidelines, and costs.
2. Interventions are measured and scored on each criterion using evidence and expert opinion. Scores are often on predefined scales.
3. Criteria weights reflecting importance are generated, commonly using analytic hierarchy process or defined scales. Weights involve multiple stakeholders.
Sensitivity analysis assesses uncertainty, though many studies do not report this. MCD
The document discusses four basic patterns of thinking: understanding what's going on, determining why things happened, deciding on a course of action, and anticipating what lies ahead. It then discusses how these patterns are used in organizational contexts through situation appraisal, problem analysis, decision analysis, and potential problem analysis. Decision analysis in particular involves analyzing reasons for decisions, options, and risks to accomplish goals. The characteristics of thinking first, seeing first, and doing first approaches to decision making are also outlined.
This document provides an overview of key concepts in decision analysis, including problem formulation, decision making without and with probabilities, risk analysis, sensitivity analysis, and computing branch probabilities. It discusses techniques like influence diagrams, payoff tables, decision trees, and the expected value, conservative, optimistic, and minimax regret approaches. It also covers risk profiles, sensitivity analysis, Bayes' theorem, and the expected value of perfect and sample information.
This presentation summarizes key aspects of decision analysis and decision making under uncertainty. It discusses decision criteria like maximax, maximin, and Laplace that can be used when outcomes are uncertain. When probabilities are known, criteria for decision making under risk are used. Sequential decisions can be modeled with decision trees, which represent decisions, chances, and outcomes with nodes and paths. The presentation was given to MBA students on the topic of decision analysis.
- The document discusses applying decision analysis and resolution (DAR) techniques in real-world situations. It describes when DAR should be used, such as for technical decisions, business decisions, project proposals, training priorities, and requirement prioritization.
- The basic DAR process involves defining the decision statement, identifying alternatives, evaluating alternatives against criteria, and making a decision. A variety of decision-making tools are presented, including grid analysis, decision trees, cost-benefit analysis, and more.
- Examples of applying techniques like grid analysis, fishbone diagrams, and decision trees are provided. Lessons learned emphasize having criteria for when to use DAR and that institutionalizing the process takes time.
This document describes a study that uses a Markov chain Monte Carlo simulation to model the rising incidence of Type II diabetes in youth ages 15-20 in the United States. The study aims to identify risk factors contributing to the rise and understand the impact on dependency ratios. Aggregate data from various sources was used to simulate disease progression and estimate covariates. The model is expected to help reformulate dependency ratios by accounting for the effects of morbidity on labor participation.
Environmental Health:Economic Costs of Environmental Damage And Suggested Pri...No to mining in Palawan
Environmental Health:
Economic Costs of Environmental Damage
And Suggested Priority Interventions
A Contribution to the Philippines
Country Environmental Analysis
Submitted to
The World Bank
Final Report
March 31, 2009
The results indicate that the economic costs of pollution and sanitation-related
health effects are high and cannot be ignored. The combined costs for all three sectors in 2003 totaled PhP 42.4 billion (USD 783.2 million) in lost productivity due to premature deaths or PhP 168.4 billion (USD 3.1 billion) in terms of value of statistical life (Table1). In addition, the cost of morbidity was PhP 18.3 billion (USD 337.6 million), comprising of loss in productivity totaling PhP 10.4 billion (USD 191.3 million), direct costs to Filipino households to treat these illnesses totaling PhP 6.4 billion (USD 118.7 million), and the cost to the government health care insurance system—representing the subsidy for PhilHealth members’ hospitalization costs—and for general government subsidy for publicly-owned health facilities was close to PhP 1.5 billion (USD 27.6 million).
This document introduces decision-analytic modeling techniques for clinical and economic projections. It discusses why modeling is useful, provides a taxonomy of modeling techniques including Markov models, and showcases examples of models that project clinical and economic outcomes. It concludes by guiding interested audience members to further information on modeling tools, societies, journals, and educational institutions.
Multi criteria decision analysis for healthcareSunhong Kwon
Multi-criteria decision analysis (MCDA) is a process used to evaluate multiple criteria in healthcare decision making. Three key aspects of MCDA are summarized:
1. MCDA identifies criteria like clinical benefits, safety, costs, and other factors to evaluate healthcare interventions. Studies consider criteria like disease severity, population affected, clinical guidelines, and costs.
2. Interventions are measured and scored on each criterion using evidence and expert opinion. Scores are often on predefined scales.
3. Criteria weights reflecting importance are generated, commonly using analytic hierarchy process or defined scales. Weights involve multiple stakeholders.
Sensitivity analysis assesses uncertainty, though many studies do not report this. MCD
The document discusses four basic patterns of thinking: understanding what's going on, determining why things happened, deciding on a course of action, and anticipating what lies ahead. It then discusses how these patterns are used in organizational contexts through situation appraisal, problem analysis, decision analysis, and potential problem analysis. Decision analysis in particular involves analyzing reasons for decisions, options, and risks to accomplish goals. The characteristics of thinking first, seeing first, and doing first approaches to decision making are also outlined.
This document provides an overview of key concepts in decision analysis, including problem formulation, decision making without and with probabilities, risk analysis, sensitivity analysis, and computing branch probabilities. It discusses techniques like influence diagrams, payoff tables, decision trees, and the expected value, conservative, optimistic, and minimax regret approaches. It also covers risk profiles, sensitivity analysis, Bayes' theorem, and the expected value of perfect and sample information.
This presentation summarizes key aspects of decision analysis and decision making under uncertainty. It discusses decision criteria like maximax, maximin, and Laplace that can be used when outcomes are uncertain. When probabilities are known, criteria for decision making under risk are used. Sequential decisions can be modeled with decision trees, which represent decisions, chances, and outcomes with nodes and paths. The presentation was given to MBA students on the topic of decision analysis.
- The document discusses applying decision analysis and resolution (DAR) techniques in real-world situations. It describes when DAR should be used, such as for technical decisions, business decisions, project proposals, training priorities, and requirement prioritization.
- The basic DAR process involves defining the decision statement, identifying alternatives, evaluating alternatives against criteria, and making a decision. A variety of decision-making tools are presented, including grid analysis, decision trees, cost-benefit analysis, and more.
- Examples of applying techniques like grid analysis, fishbone diagrams, and decision trees are provided. Lessons learned emphasize having criteria for when to use DAR and that institutionalizing the process takes time.
This document describes a study that uses a Markov chain Monte Carlo simulation to model the rising incidence of Type II diabetes in youth ages 15-20 in the United States. The study aims to identify risk factors contributing to the rise and understand the impact on dependency ratios. Aggregate data from various sources was used to simulate disease progression and estimate covariates. The model is expected to help reformulate dependency ratios by accounting for the effects of morbidity on labor participation.
Environmental Health:Economic Costs of Environmental Damage And Suggested Pri...No to mining in Palawan
Environmental Health:
Economic Costs of Environmental Damage
And Suggested Priority Interventions
A Contribution to the Philippines
Country Environmental Analysis
Submitted to
The World Bank
Final Report
March 31, 2009
The results indicate that the economic costs of pollution and sanitation-related
health effects are high and cannot be ignored. The combined costs for all three sectors in 2003 totaled PhP 42.4 billion (USD 783.2 million) in lost productivity due to premature deaths or PhP 168.4 billion (USD 3.1 billion) in terms of value of statistical life (Table1). In addition, the cost of morbidity was PhP 18.3 billion (USD 337.6 million), comprising of loss in productivity totaling PhP 10.4 billion (USD 191.3 million), direct costs to Filipino households to treat these illnesses totaling PhP 6.4 billion (USD 118.7 million), and the cost to the government health care insurance system—representing the subsidy for PhilHealth members’ hospitalization costs—and for general government subsidy for publicly-owned health facilities was close to PhP 1.5 billion (USD 27.6 million).
This document introduces decision-analytic modeling techniques for clinical and economic projections. It discusses why modeling is useful, provides a taxonomy of modeling techniques including Markov models, and showcases examples of models that project clinical and economic outcomes. It concludes by guiding interested audience members to further information on modeling tools, societies, journals, and educational institutions.
APIFARMA, the Portuguese pharmaceutical industry assocation, holds a series of conference throughout they year. OHE's Jorge Mestre-Ferrandiz, an expert on pricing and reimbursement (P&R) in Europe, was the lead speaker at the October 2014 conference on access to innovation. His presentation covers existing and potential approaches to evaluating new medicines as a condition for P&R in France, Germany and the UK.
The document discusses a cost analysis of various metal hydroxide chemical (MHC) technologies used in printed wiring board manufacturing. It presents a framework for a hybrid cost formulation that uses computer simulation and activity-based costing to evaluate and compare the costs of different MHC processes. Key cost components considered include capital, production, maintenance, energy and water consumption, wastewater generation, and environmental costs. The analysis aims to determine the costs of fully operational MHC lines for a sample job size and evaluate the effects of critical cost variables through sensitivity analysis.
Oncopharmacoeconomy ii, Prof. Dr. F. Cankat TulunayF. Cankat Tulunay
The document discusses pharmacoeconomic analysis and clinical trials. Pharmacoeconomic analysis is more concerned with what happens in real-life settings, examines effectiveness, and measures outcomes like resource consumption and quality of life. Clinical trials focus on efficacy and safety in a controlled setting. The document provides examples of pharmacoeconomic thresholds used to determine cost-effectiveness of treatments, such as $50,000 per QALY gained, and discusses value-based pricing models.
The document discusses some basic probability concepts including experiments, outcomes, random variables, sample spaces, events, discrete and continuous random variables, probability distributions, expected values, and summation rules. It provides examples like tossing a coin or die to illustrate key terms and explains concepts like joint, marginal, and conditional probabilities for random variables. Formulas are given for probability, expected value, and summation to calculate totals and averages.
This document defines different types of costs that businesses deal with and provides examples. It discusses direct and indirect costs, fixed and variable costs, as well as other cost types like average, marginal, historical, predetermined and opportunity costs. Direct costs are those directly related to production while indirect costs cannot be directly assigned to a single product. Costs can also be classified as fixed, variable, or semi-variable depending on how they change with business activity.
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
The document provides an overview of decision tree learning algorithms:
- Decision trees are a supervised learning method that can represent discrete functions and efficiently process large datasets.
- Basic algorithms like ID3 use a top-down greedy search to build decision trees by selecting attributes that best split the training data at each node.
- The quality of a split is typically measured by metrics like information gain, with the goal of creating pure, homogeneous child nodes.
- Fully grown trees may overfit, so algorithms incorporate a bias toward smaller, simpler trees with informative splits near the root.
This document discusses several methods for valuing human resources in accounting: opportunity cost, standard cost, current purchase cost, and economic value. It defines human resource accounting and explains the importance and objectives of valuing employees. The opportunity cost method values employees based on their value in alternative uses within an organization. The standard cost method establishes standard costs per employee grade that are updated annually.
Economic Costs and Benefits of Beijing Olympics 2008 (IB Geography - Leisure,...Enoch Yambilla
Hosting the 2008 Olympics in Beijing, China provided both economic and social benefits but also disadvantages. Economically, China gained infrastructure improvements, tourism revenue, and new jobs. Socially, the Olympics raised China's international profile and increased enthusiasm for sports. However, some residents were displaced and social conflicts arose due to overcrowding from visitors. Large costs were incurred to host the Games and left debt afterwards.
1. Markov processes can be used to model systems that transition between states based on probabilities. The document discusses several applications of Markov processes including calculating steady state probabilities, absorption probabilities, and expected times to absorption.
2. As an example, the document examines a phone company problem where calls arrive as a Poisson process and call durations are exponentially distributed. It shows how to set up and solve the balance equations to find steady state probabilities.
3. Other examples covered include finding the probability of an absent-minded professor getting wet during rain and calculating absorption probabilities and expected times for an example Markov chain. The document also discusses mean first passage and recurrence times.
This document provides an overview of different types of costs that are relevant for business. It defines and gives examples of various costs including actual costs, opportunity costs, sunk costs, incremental costs, explicit costs, implicit costs, book costs, out of pocket costs, accounting costs, economic costs, direct costs, indirect costs, controllable costs, non-controllable costs, historical costs, replacement costs, shutdown costs, abandonment costs, urgent costs, business costs, fixed costs, variable costs, total costs, average costs, marginal costs, short run costs, and long run costs. The document is a presentation on costs submitted by a student for their coursework.
This document provides an overview of Hidden Markov Models (HMM). HMMs are statistical models used to model systems where an underlying process produces observable outputs. In HMMs, the observations are modeled as a Markov process with hidden states that are not directly observable, but can only be inferred through the observable outputs. The document describes the key components of HMMs including transition probabilities, emission probabilities, and the initial distribution. Examples of applications like speech recognition and bioinformatics are provided. Finally, common algorithms for HMMs like Forward, Baum-Welch, Backward, and Viterbi are listed for performing inference on the hidden states given observed sequences.
This document provides an overview of decision analysis and decision making under certainty and uncertainty. It describes decision environments like certainty, where outcomes are known, and uncertainty, where outcomes are unknown. It also defines decision criteria for nonprobabilistic decisions, where probabilities are unknown, and probabilistic decisions, which consider probabilities. Examples are given of decision criteria like expected value, maximax, maximin and minimax regret. Payoff tables, opportunity loss tables, and decision trees are used to demonstrate the application of these decision criteria.
This document provides an overview of key concepts in health economics. It discusses the definition and scope of health economics, as well as important microeconomic and macroeconomic factors that influence the health sector. Some key methods covered include economic evaluation techniques like cost-effectiveness analysis, cost-benefit analysis, and cost-utility analysis. Health outcomes measures like QALYs and DALYs are also explained. The document aims to introduce foundational ideas around applying economic principles and evaluation to issues of health, healthcare delivery, and resource allocation.
Economic analysis for different levels of decision makingHenk Hogeveen
I was invited to give a keynote presentation for the German languaged Epidemiology meeting which was held last week in Zurich, Switzerland. My presentation gave an overview of the decision problem in animal health and gives some examples of economic analyses that have been made at different levels of decision making. Specific items were: dry cow therapy, Q fever and BSE
This document provides an introduction to hidden Markov models (HMMs). It defines HMMs as an extension of Markov models that allows for observations that are probabilistic functions of hidden states. The core problems of HMMs are finding the probability of an observed sequence and determining the most probable hidden state sequence that produced an observation. HMMs have applications in areas like speech recognition by finding the most likely string of words given acoustic input using the Viterbi and forward algorithms.
APIFARMA, the Portuguese pharmaceutical industry assocation, holds a series of conference throughout they year. OHE's Jorge Mestre-Ferrandiz, an expert on pricing and reimbursement (P&R) in Europe, was the lead speaker at the October 2014 conference on access to innovation. His presentation covers existing and potential approaches to evaluating new medicines as a condition for P&R in France, Germany and the UK.
The document discusses a cost analysis of various metal hydroxide chemical (MHC) technologies used in printed wiring board manufacturing. It presents a framework for a hybrid cost formulation that uses computer simulation and activity-based costing to evaluate and compare the costs of different MHC processes. Key cost components considered include capital, production, maintenance, energy and water consumption, wastewater generation, and environmental costs. The analysis aims to determine the costs of fully operational MHC lines for a sample job size and evaluate the effects of critical cost variables through sensitivity analysis.
Oncopharmacoeconomy ii, Prof. Dr. F. Cankat TulunayF. Cankat Tulunay
The document discusses pharmacoeconomic analysis and clinical trials. Pharmacoeconomic analysis is more concerned with what happens in real-life settings, examines effectiveness, and measures outcomes like resource consumption and quality of life. Clinical trials focus on efficacy and safety in a controlled setting. The document provides examples of pharmacoeconomic thresholds used to determine cost-effectiveness of treatments, such as $50,000 per QALY gained, and discusses value-based pricing models.
The document discusses some basic probability concepts including experiments, outcomes, random variables, sample spaces, events, discrete and continuous random variables, probability distributions, expected values, and summation rules. It provides examples like tossing a coin or die to illustrate key terms and explains concepts like joint, marginal, and conditional probabilities for random variables. Formulas are given for probability, expected value, and summation to calculate totals and averages.
This document defines different types of costs that businesses deal with and provides examples. It discusses direct and indirect costs, fixed and variable costs, as well as other cost types like average, marginal, historical, predetermined and opportunity costs. Direct costs are those directly related to production while indirect costs cannot be directly assigned to a single product. Costs can also be classified as fixed, variable, or semi-variable depending on how they change with business activity.
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
The document provides an overview of decision tree learning algorithms:
- Decision trees are a supervised learning method that can represent discrete functions and efficiently process large datasets.
- Basic algorithms like ID3 use a top-down greedy search to build decision trees by selecting attributes that best split the training data at each node.
- The quality of a split is typically measured by metrics like information gain, with the goal of creating pure, homogeneous child nodes.
- Fully grown trees may overfit, so algorithms incorporate a bias toward smaller, simpler trees with informative splits near the root.
This document discusses several methods for valuing human resources in accounting: opportunity cost, standard cost, current purchase cost, and economic value. It defines human resource accounting and explains the importance and objectives of valuing employees. The opportunity cost method values employees based on their value in alternative uses within an organization. The standard cost method establishes standard costs per employee grade that are updated annually.
Economic Costs and Benefits of Beijing Olympics 2008 (IB Geography - Leisure,...Enoch Yambilla
Hosting the 2008 Olympics in Beijing, China provided both economic and social benefits but also disadvantages. Economically, China gained infrastructure improvements, tourism revenue, and new jobs. Socially, the Olympics raised China's international profile and increased enthusiasm for sports. However, some residents were displaced and social conflicts arose due to overcrowding from visitors. Large costs were incurred to host the Games and left debt afterwards.
1. Markov processes can be used to model systems that transition between states based on probabilities. The document discusses several applications of Markov processes including calculating steady state probabilities, absorption probabilities, and expected times to absorption.
2. As an example, the document examines a phone company problem where calls arrive as a Poisson process and call durations are exponentially distributed. It shows how to set up and solve the balance equations to find steady state probabilities.
3. Other examples covered include finding the probability of an absent-minded professor getting wet during rain and calculating absorption probabilities and expected times for an example Markov chain. The document also discusses mean first passage and recurrence times.
This document provides an overview of different types of costs that are relevant for business. It defines and gives examples of various costs including actual costs, opportunity costs, sunk costs, incremental costs, explicit costs, implicit costs, book costs, out of pocket costs, accounting costs, economic costs, direct costs, indirect costs, controllable costs, non-controllable costs, historical costs, replacement costs, shutdown costs, abandonment costs, urgent costs, business costs, fixed costs, variable costs, total costs, average costs, marginal costs, short run costs, and long run costs. The document is a presentation on costs submitted by a student for their coursework.
This document provides an overview of Hidden Markov Models (HMM). HMMs are statistical models used to model systems where an underlying process produces observable outputs. In HMMs, the observations are modeled as a Markov process with hidden states that are not directly observable, but can only be inferred through the observable outputs. The document describes the key components of HMMs including transition probabilities, emission probabilities, and the initial distribution. Examples of applications like speech recognition and bioinformatics are provided. Finally, common algorithms for HMMs like Forward, Baum-Welch, Backward, and Viterbi are listed for performing inference on the hidden states given observed sequences.
This document provides an overview of decision analysis and decision making under certainty and uncertainty. It describes decision environments like certainty, where outcomes are known, and uncertainty, where outcomes are unknown. It also defines decision criteria for nonprobabilistic decisions, where probabilities are unknown, and probabilistic decisions, which consider probabilities. Examples are given of decision criteria like expected value, maximax, maximin and minimax regret. Payoff tables, opportunity loss tables, and decision trees are used to demonstrate the application of these decision criteria.
This document provides an overview of key concepts in health economics. It discusses the definition and scope of health economics, as well as important microeconomic and macroeconomic factors that influence the health sector. Some key methods covered include economic evaluation techniques like cost-effectiveness analysis, cost-benefit analysis, and cost-utility analysis. Health outcomes measures like QALYs and DALYs are also explained. The document aims to introduce foundational ideas around applying economic principles and evaluation to issues of health, healthcare delivery, and resource allocation.
Economic analysis for different levels of decision makingHenk Hogeveen
I was invited to give a keynote presentation for the German languaged Epidemiology meeting which was held last week in Zurich, Switzerland. My presentation gave an overview of the decision problem in animal health and gives some examples of economic analyses that have been made at different levels of decision making. Specific items were: dry cow therapy, Q fever and BSE
This document provides an introduction to hidden Markov models (HMMs). It defines HMMs as an extension of Markov models that allows for observations that are probabilistic functions of hidden states. The core problems of HMMs are finding the probability of an observed sequence and determining the most probable hidden state sequence that produced an observation. HMMs have applications in areas like speech recognition by finding the most likely string of words given acoustic input using the Viterbi and forward algorithms.