Decision Analysis

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

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.)

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

Decision Analysis: The PROACTIVE framework Make problem and its objectives explicit Make problem and its objectives explicit List alternative actions List alternative actions How actions alter subsequent events with probabilities, values and trade-offs How actions alter subsequent events with probabilities, values and trade-offs Synthesise balance of benefits and harms of each alternative 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 Normally uses framework of decision trees

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

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

An Example Should a health care worker who has a needlestick injury be given HIV prophylaxis treatment? Should a health care worker who has a needlestick injury be given HIV prophylaxis treatment? HIV an incurable chronic illness HIV an incurable chronic illness There is a risk of infection from needlestick injury There is a risk of infection from needlestick injury Prophylaxis treatment can be given to prevent HIV infection, but side effects can be problematic 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

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

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

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

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

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

Consequences and chances: Balance sheet BenefitHarm No prophylaxis No side effects from treatment No unnecessary treatment Risk of developing HIV Selective prophylaxis Reduced risk of developing HIV Risk of developing HIV if injury/patient not perceived to be high risk Side effects from treatment Prophylaxis may not work Routine prophylaxis Reduced risk of developing HIV May have unnecessary treatment Side effects from treatment Prophylaxis may not work

Modelling the consequences

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

Identifying the chances Average risk for HIV transmission after percutaneous exposure to HIV infected blood is approximately 0.3% 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) 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%) 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, 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,

Probabilities in the tree

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

Measuring values Need a strategy that weighs harms and benefits explicitly in accordance with values of population/individual Need a strategy that weighs harms and benefits explicitly in accordance with values of population/individual Types of outcome Types of outcome Two possible outcomes – no need for explicit value assessment as chose the strategy that gives highest probability of better 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) 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 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

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 (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 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. 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.

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

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

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

Example: Values ‘Off the shelf’ measures ‘Off the shelf’ measures Existing preference scores associated with HIV infection and some side effects Existing preference scores associated with HIV infection and some side effects Preference scores for HIV range from 0.5 – 0.75 Preference scores for HIV range from 0.5 – 0.75 Nausea and vomiting – Nausea and vomiting – Diarrohea – 0.81 Diarrohea – 0.81 Abdominal pain – Abdominal pain – Values used in model – HIV infection 0.5, side effects 0.9, no infection 1.0. Infection and side effects – 0.4 (my value) 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),

Full analysis

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

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

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

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

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

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