1. Decision Analytic Approaches for Evidence-Based Practice M8120 Fall 2001 Suzanne Bakken, RN, DNSc, FAAN School of Nursing & Department of Medical Informatics Columbia University

2. Outline Health care decision making Expected value decision making Building a decision tree with Data

3. What is a Decision? A decision is an irreversible choice among alternative ways to allocate valuable resources

4. What makes a decision hard? Complexity Uncertainty including limited information Dynamic effects High stakes Unclear alternatives Unclear preferences

5. Are These Decisions? A California utility faces likely electrical power shortages and is considering constructing a power plant using either coal or nuclear energy. You are concerned whether you could have found a better deal on the CD player you just bought. A person with appendicitis is uncertain whether there will be unpleasant side effects from the appendectomy he is about to have. A graduate student is considering whether to pay a mechanic to fix her 8 year old car or trade it in on a newer model. An oil company is attempting to estimate oil prices one year from now. You are trying to decide if you should have the birthday party you’re planning for a friend outside.

6. Anatomy of a Health Practice Decision Goal - to choose the action that is most likely to deliver the outcomes that patients find desirable Outcomes of alternative practices must be estimated (primarily analytical) –Collection and analysis of evidence regarding benefits, harms, and costs of each option –Subjective judgment Desirability of outcomes of each option must be compared (patient preferences/utilities) –Benefits compared with harms –Outcomes versus cost –Resources consumed(Eddy, 1990)

7. Decision Analysis: Expected Value Decision Making Prescriptive Analytic Explicit

8. Basic Concepts Biological events random Outcomes of illness uncertain Outcomes of treatments uncertain Must choose between treatments - a gamble Utility - a measure of preference Expected value - result expected on average

9. Quantifying Uncertainty Probability as a language for expressing uncertainty Bayes’ theorum for probability revision

10. Probability Fundamentals Strength of belief A number between 0 and 1 that expresses an opinion about the likelihood of an event Probability of an event that is certain to occur is 1 Probability of an event that is certain to NOT occur is 0

11. Definitions Prior probability - the probability of an event before new information (finding) is acquired; pretest probability or risk Posterior probability - the probability of an event after new information (finding) is acquired; posttest probability or risk Probability revision - taking new information into account by converting prior probability to posterior probability

12. Role of Probability Revision Techniques Abnormal Finding Diagnosis Before Finding After Finding 0 1 Probability of Disease Prior Probability Posterior Probability

13. Role of Probability Revision Techniques Negative Finding Diagnosis After Finding Before Finding 0 1 Probability of Disease Posterior Probability Prior Probability

14. Steps in Decision Analysis Create a decision tree –Identify and bound problem –Structure the problem –Characterize information needed Calculate the expected value of each decision alternative Choose the decision alternative with the highest expected value (payoff, utility) Use sensitivity analysis to test the conclusions of the analysis

15. Whose View? Individual patient Physician Society Government Healthcare institutions

16. Create the Decision Tree Define the decision problem Identify the decision alternatives List the possible clinical outcomes of each of the decision alternatives Represent the sequence of events leading to the clinical outcomes by a series of chance nodes and decision nodes Choose a time horizon for the problem Determine the probability of each chance outcome Assign a value (preference, utility, payoff) to each clinical outcome

17. Simple Decision Tree Operate Do not operate Disease present Disease absent Outcome; Treatment with disease Outcome; Treatment without disease Outcome; Treatment with disease Outcome; Treatment without disease

18. Represent Sequence of Events Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative Death Palliate Operative Death Survive No cure Cure No Cure No cure Cure Try for the cure

19. Determine Probability of Each Chance Outcome Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate Operative death Survive No cure Cure No Cure No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.02 p=.98 p=.10 p=.90 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure

20. Assign Values Utilities, preferences, payoffs –Mortality –Length of survival –Cost –Quality of life –Quality of life years

21. Standard gamble

22. Assigning Values to the Decision Alternatives Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate Operative death U=0 Survive No cure Cure No Cure No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.02 p=.98 p=.10 p=.90 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure U=2 U=20 U=2 U=20 U=2 U=20 U=0

23. Path Probability Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate Operative death Survive No cure Cure No Cure No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.02 p=.98 p=.10 p=.90 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure Path probability of a sequence of chance events is the product of all probabilities along that sequence (summation principle)

24. Folding Back the Tree Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate Operative death U=0 Survive No cure Cure No Cure No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.02 p=.98 p=.10 p=.90 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure U=2 U=20 U=2 U=20 U=2 U=20 U=0

25. Folding Back the Tree Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate Operative death U=0 Survive U=20 Survive No cure Cure No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.02 p=.98 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure U=2 U=20 U=2 U=20 U=0.1 X 20 +.90 X 2 = 3.8

26. Fold It Again Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate Operative death U=0 Survive U=20 Survive No cure Cure No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.02 p=.98 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure U=2 U=20 U=2 U=20 U=0.1 X 20 +.90 X 2 = 3.8

27. Fold It Again Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate Operative death U=0 Survive No cure Cure No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure U=2 U=20 U=2 U=20 U=0 U =.98 X 3.8 +.02 X 0 = 3.72

28. Try for Cure Vs. Palliative Operate Do not operate Disease present Disease absent Disease present Disease absent Survive Operative death Palliate No cure Cure p=.10 p=.90 p=.10 p=.90 p=.10 p=.01 p=.99 Try for the cure U=2 U+20 U=20 U=0 U =.98 X 3.8 +.02 X 0 = 3.72 U =.90 X 18.2 +.10 X 0 = 16.38

29. Final Fold - Operate Vs. Do Not Operate Do not operate Operate U=18.38 U=19.46

30. Sensitivity Analysis: What Happens if Probability of Disease Changes? Should the decision still be operate?

31. Sensitivity Analysis: What Happens if Operative Mortality Changes? Should the decision still be operate?

32. Expected Value Decision Making Data for Healthcare Exercise #1