Decision Analysis

1. Decision Analysis Prof. Carl Thompson cat4@york.ac.uk

2. What is decision analysis? • Based on a normative theory of decision making: subjective expected utility theory (SEUT) • Assumes • 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 understanding of a decision task • Divides decision task into its components • Use of decision trees as a way of structuring the decision task • Uses evidence in the form of probabilities – so can examine the risks associated with different options • Examines the utility or cost associated with each option • Suggests the most appropriate decision option for that particular situation

5. Stages in a decision analysis • Structure the problem as a decision tree - identifying choices, information (what is and is not known) and preferences • assess numerically the probability (chances) of every choice branch • assess numerically the utility (preferences) of every outcome state • identify option which maximises expected utility • carry out a sensitivity analysis to explore effect of varying judgements • ‘toss -up’ if two options have same EU (Dowie, 1993)

6. Structure the problem Elizabeth Harding: 78 year old lady with atrial fibrillation (AF), currently not on any anticoagulation therapy. The decision problem: AF is associated with an increased risk of thromboembolic stroke. Treatment with anticoagulation therapy can reduce the risk of stroke, however there is an associated risk of bleeding. If prescribed Warfarin, then regular monitoring of INR is needed. If prescribed aspirin, there is a risk of gastrointestinal side effects.

7. Structure the problem • Illustrated using a decision tree • structure of tree dictates values needed and outcomes considered. • should be structured with the aid of an expert in the field • need for compromise between simplifying problem too much and including enough detail for it to be relevant to the problem (Doubilet & McNeil, 1988

8. 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

9. Consequences or 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

10. Clinical problem • Alternatives for Mrs Harding include: • No treatment • Prescription of Warfarin • Prescription of Aspirin • Consequences or chances include: • Stroke (either through thromboemolism or bleed) • Which may or may not lead to severe disability • Treatment side effects

12. 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 • Highlights the risks associated with each decision option • Probabilities should come from good quality research evidence

14. Assessing Utility/Preference • 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

15. Measuring utility/values • Need a strategy that weighs harms and benefits explicitly in accordance with values of population/individual • Utility measures • Are a measure of the desirability of all the possible outcomes in the decision tree • Provide a numerical value attached to beliefs and feelings • Are measured on a numerical scale where 0 = worst possible outcome and 100 = best possible outcome

16. Utility measures • 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. • Rating scales • Standard Gamble • Time trade off

17. 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

21. Sensitivity Analysis • 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

22. Sensitivity Analysis • If the value or utility of having side effects associated with the treatment (with no stroke) is less than 0.84, then no treatment is the preferred option. If the utility is above 0.84, then Warfarin is the preferred treatment

23. Sensitivity analysis • In this situation the decision is ‘preference sensitive’ with regard to how the individual feels about having side effects associated with anticoagulation therapy. • Decisions can also be ‘probability sensitive’

24. 1 way sensitivity analysis HIV prophylaxis

25. Benefits • Makes all assumptions in a decision explicit • Allows examination of the decision process used • Way of integrating evidence into the decision process • Often insight gained during process more important than the actual numbers used • Can be used for individual decisions, population level decisions and for cost-effectiveness analysis

26. Limitations • 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

27. References • Sources of Evidence • Prodigy Guidance – Atrial Fibrillation www.prodigy.nhs.uk/guidance.asp?gt=Atrial%20fibrillation • Protheroe J, Fahey T, Montgomery AA, Peters TJ (2000) The impact of patients’ preferences on the treatment of atrial fibrillation: observational study of patient based decision analysis. BMJ 320: 1380-4 • Hankey GJ, Sudlow CLM, Dunbabin DW. Thienopyridine derivatives (ticlopidine, clopidogrel) versus aspirin for preventing stroke and other serious vascular events in high vascular risk patients. The Cochrane Database of Systematic Reviews 1999, Issue 4. Art. No.: CD001246. DOI: 10.1002/14651858.CD001246. • Further Reading • Llewelyn H, Hopkins A (1993) Analysing how we reach clinical decisions. Royal College of Physicians of London.