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Decision Analysis
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  1. Decision Analysis

  2. What is decision analysis? • Based on expected utility theory • Used in conditions of uncertainty • Decision process logical and rational • Works on basis that rational decision maker will choose the option that maximises their utility (the desirability or value attached to a decision outcome)

  3. What is decision analysis? “Decision analysis is a systematic, explicit, quantitative way of making decisions in health care that can … lead to both enhanced communication about clinical controversies and better decisions.” (Hunink, Glasziou et al, 2001, p.3.)

  4. What is decision analysis? • Assists in comprehension of problem • Divides logical structure of decision problem into its components • Uses evidence in the form of probabilities • Analysed individually • Recombined systematically • Suggests a decision • Use of decision trees as a way of structuring the problem

  5. Decision Analysis: The PROACTIVE framework • Make problem and its objectives explicit • List alternative actions • How actions alter subsequent events with probabilities, values and trade-offs • Synthesise balance of benefits and harms of each alternative Problem, Reframe, Objectives, Alternatives, Consequences and chances, Trade-offs, Intergrate, Value, Explore and evaluate • Normally uses framework of decision trees

  6. Problem and Objectives • Need to ensure addressing the right problem • Define the problem • Reframe the problem from other perspectives • Identify fundamental objectives for any course of action

  7. Problem and Objectives • What would happen if I did nothing? • Outcomes avoid/achieve • Reframing • What are the limits on resources, patient perspectives, provider perspectives, policy maker? • Objectives • What elements are of most concern to the patient/population?

  8. An Example • Should a health care worker who has a needlestick injury be given HIV prophylaxis treatment? • HIV an incurable chronic illness • There is a risk of infection from needlestick injury • Prophylaxis treatment can be given to prevent HIV infection, but side effects can be problematic Public Health Service guidelines for the management of health-care worker exposures to HIV and recommendations for postexposure prophylaxis. Centers for Disease Control and Prevention. MMWR – Morbidity and Mortality Weekly Report 47(RR-7): 1-33, 1998

  9. Problem and Objectives • Problem • Should all health care workers who receive a needlestick injury receive prophylaxis treatment for HIV? • Reframe • What is the risk of infection after needlestick? What drugs are available for prophylaxis? How effective are they? What are their side effects? • Objective • To determine if a health care worker who has a needlestick injury should have prophylaxis treatment for HIV

  10. Alternatives, Consequences and Trade-offs • Range of reasonable alternatives • Three categories; • Watchful waiting • Intervention • More information before deciding • Can be illustrated using a decision tree

  11. The structure of a decision tree • Square node • Decision node • Represents choice between actions • Circle node • Chance node • Represents uncertainty • Potential outcomes of each decision

  12. Consequences and chances • Consequences of each decision option and chance of event occurring • Short term and long term • Need best available evidence • Includes risks and benefits of interventions • Natural history of disease • Accuracy and interpretation of diagnostic test information

  13. Example: Alternatives • Alternatives for treating needlestick injuries include: • No prophylaxis • Use of prophylaxis selectively dependent on injury and perceived risk from patient • Routine prophylaxis treatment for all injuries

  14. Consequences and chances: Balance sheet

  15. Modelling the consequences

  16. Chances • Use probability or chance of events occurring • For each ‘branch’ in the decision tree, values have to add up to 1 or 100% • Specific measures of the uncertainty associated with the decision • Probabilities should come from good quality research evidence

  17. Identifying the chances • Average risk for HIV transmission after percutaneous exposure to HIV infected blood is approximately 0.3% • Effectiveness of prophylaxis difficult to estimate – a case control study indicated that prophylaxis reduced odds of HIV infection by 81%. (If change this to percentages – if take prophylaxis 5% chance will develop HIV) • Side effects include nausea/vomiting, malaise/fatigue, headache, myalgia, abdominal pain, diarrohea. Probability of getting a side effect 50-75%. (Figure used 63%) Public Health Service guidelines for the management of health-care worker exposures to HIV and recommendations for postexposure prophylaxis. Centers for Disease Control and Prevention. MMWR – Morbidity and Mortality Weekly Report 47(RR-7): 1-33, 1998. Cardo, D., Culver, D. et al (1997) A case-control study of HIV seroconversion in health care workers after percutaneous exposure. New England Journal of Medicine 337:21, 1485-1490

  18. Probabilities in the tree

  19. Identifying and estimating the value of Trade-offs • When there is more than one type of consequence – valuation important • Trade-offs between benefits and potential harms of consequences • Need clarification of the values involved • Choice of intervention will often depend on the values of the decision maker • When considering values, need to consider whether individual or societal

  20. Measuring values • Need a strategy that weighs harms and benefits explicitly in accordance with values of population/individual • Types of outcome • Two possible outcomes – no need for explicit value assessment as chose the strategy that gives highest probability of better outcome • Single-attribute case – spectrum of outcomes from least to most preferred (e.g. survival time) • Multi-attribute case – two or more dimensions or values (e.g. life expectancy and quality of life). Easier if can be measured on a single, generic scale

  21. Measuring values • Utility (value) measures different from quality of life measures – reflect how respondent values a state of health, not just the characteristics of the health state • Utility scale – can be averaged out in a decision tree without distorting preferences of individual represented. Normally measured from 0 = DEATH to 1 = PERFECT HEALTH • Quality Adjusted Life Years (QALY) commonly used for population utility measures – 1 year in perfect health = 1 QALY. Health states measured against this (e.g. 2 years in health rated as 0.5 of perfect health = 1 QALY) Considers quantity and quality of life.

  22. Measuring values • Rating scale • Global measure • Easily explained and easy to measure • Not a true utility • Standard Gamble • Grounded in expected utility theory • Assesses utility for a health state by asking how high a risk of death would accept to improve it • Ask to choose between life in given state and a gamble between perfect health and death

  23. Measuring values • Time trade-off • Utility assessed by asking how much time would give up to improve it • Choose between set length of life in given health state and shorter length of life in perfect health • Utility given by ratio of shorter to longer life expectancy • Other techniques • Willingness to pay • Health indexes (e.g. Health Utilities Index (HUI), EuroQol). Use mapping rules to translate QOL measures into utilities

  24. Calculating expected utility • Values are placed in decision tree by appropriate outcomes • Expected value for each branch calculated by multiplying utility with probability • Expected values for each branch of tree added together to give EU for each decision option • Depending on nature of values, option with highest/lowest value is the option that should be taken

  25. Example: Values • ‘Off the shelf’ measures • Existing preference scores associated with HIV infection and some side effects • Preference scores for HIV range from 0.5 – 0.75 • Nausea and vomiting – 0.9863 • Diarrohea – 0.81 • Abdominal pain – 0.9863 • Values used in model – HIV infection 0.5, side effects 0.9, no infection 1.0. Infection and side effects – 0.4 (my value) Bell, C.M., Richard, H. Et al (2001) A comprehensive catalog of preference scores from published cost-utility analyses. Medical Decision Making 21(4), 288-294

  26. Full analysis

  27. Explore assumptions • Necessary if numbers used in analysis are uncertain • Allows you to examine the effect different values will have on outcome • Known as sensitivity analysis • vary uncertain variables over range that is considered plausible • Can calculate effect of uncertainty on decision

  28. Example • Varied probabilities • Risk for HIV transmission after percutaneous exposure to HIV infected blood CI- 0.2%-0.5% • Probability of getting a side effect 50-75%. • Varied utilities • Preference associated with HIV infection 30 - 75

  29. Sensitivity Analysis • If the probability of no side effects is less than 0.432, then optimum decision is no prophylaxis • If the probability of no side effects is greater than 0.432, then the optimum decision is prophylaxis

  30. Sensitivity Analysis • Optimum decision also affected by the probability of getting side effects from the treatment • Varying the probability of getting HIV, or the preferences associated with having HIV have no effect on the optimum decision

  31. Benefits of Decision Analysis • Makes all assumptions in a decision explicit • Allows examination of the decision process used • Often insight gained during process more important than the actual numbers used

  32. Limitations of decision analysis • Probability estimates • often data sets needed to estimate probability don’t exist • Subjective probability estimates are open to bias: overconfidence & heuristics • Utility measures • often ask individuals to rate a state of health that they have no experience of • Different techniques will result in different numbers • Subject to framing effects