1. 1 DECISION MAKING Suppose your patient (from the Brazilian rainforest) has tested positive for a rare but serious disease. Treatment exists but is risky and costly. Therefore, it is important to make sure the disease is present prior to giving treatment. Previous studies have shown that Disease A occurs in 10 out of 10,000 persons within the Brazilian rainforest. The test for the disease is positive in 90% of patients with the disease. The test is negative in 90% of the patients without the disease. In light of this information what is the probability that your patient has Disease A?

2. 2 DECISION MAKING Much of the early decision-making work (e.g., from economics) assumed that the decision-maker will take all the relevant factors into account and make the best decision possible. However, research on decision making shows that people are far from optimal decision makers. Today, we will examine a variety of ways in which people do not give optimal decisions (including in medical settings) and try to understand what they are doing and why.

3. 3 DECISION MAKING (a sampling) (I)Cognitive Heuristics and Decision Making (II)Other Factors Leading to Cognitive Bias * Framing, Confirmation bias, overconfidence (III)Probabilistic Reasoning in Medicine * Applications of Bayes Theorem

4. 4 (I) Cognitive Heuristics & Decision Making Problem: Too much information Cognitive heuristics - often effective rules of thumb that are used to simplify decision making & reduce memory load. Used frequently by “experts” (including physicians) & “novices” Can be effective in some cases and very misleading in other cases.

5. 5 Availability Heuristic (A)Availability - refers to the tendency to make a judgment on the basis of what can easily be brought to mind. * Convenient way to estimate prior probability, in that more frequent events are usually recalled more easily. * Availability heuristic works well as long as the retrieval process is unbiased. BUT … many factors can influence accessibility of information & consequently judgments. ** recency** salience** simplicity Examples: are there more English words that begin with t or k? are there more English words that end in “_n_” or “ing”?

6. 6 Availability and estimates of deaths How do people estimate how frequently deaths occur? Influenced by availability

7. 7 (II) Other Factors Leading to Cognitive Bias (A)ONLY for students with last names beginning A-L Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequences of the programs are as follows: If Program A is adopted, 200 people will be saved. If Program B is adopted, there will be a 1/3 probability that 600 people will be saved and a 2/3 probability that no people will be saved. Which of these two programs would you favor?

8. 8 (II) Other Factors Leading to Cognitive Bias (A)ONLY for students with last names beginning M-Z Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequences of the programs are as follows: If Program C is adopted 400 people will die. If Program D is adopted there is a 1/3 probability that nobody will die and a 2/3 probability that 600 people will die. Which of these two program would you favor?

9. 9 (II) Other Factors Leading to Cognitive Bias (A)Framing effects - The same information presented in different forms can lead to different decisions. * Equivalent gains and losses should have the same effects on selection of an action. BUT … they do not. Choices involving gains (lives saved) are usually risk averse. Program A favored over B. Choices involving losses (people dead) are usually risk taking. Program D favored over C. Evidence that physician (and patient) judgments of preference for treatments affected by whether the treatment outcomes were described in terms of the probability of living or the probability of dying. (McNeil et al., 1982)

10. 10 (B) Confirmation Bias - tendency to seek out information that could only confirm our ideas. * Produces an inertia which favors the initial hypothesis. * Leads to the avoidance of information or tests which could disconfirm (e.g. negative evidence) our hypothesis. ** for example, looking for the lack of a symptom to disconfirm a hypothesis * Occurs with scientists & nonscientists alike

11. 11 (C)Overconfidence in judgment - both novices & experts appear to be more confident in their judgments than is objectively justifiable. Why? Partly due to lack of feedback concerning outcomes

12. 12 (D) Hindsight bias - tendency for people to think after the fact that they would have known something before the fact when in actuality they would not. Arkes et al. (1981) -- physicians given medical history and asked to assign probability of each of 4 diseases, given this history. Group A -- assigns probabilities Group B -- told correct diagnosis and then assigns probabilities, but told to just use medical history, as if they had not been given other information. RESULT: Group B gives 2-3 times more weight to correct diagnosis than Group A. Cannot ignore diagnosis. (Maybe leads to more overconfidence?)

13. 13 (III) Probabilistic Reasoning in Medicine Suppose your patient (from the Brazilian rainforest) has tested positive for a rare but serious disease. Treatment exists but is risky and costly. Therefore, it is important to make sure the disease is present prior to giving treatment. Previous studies have shown that Disease A occurs in 10 out of 10,000 persons within the Brazilian rainforest. The test for the disease is positive in 90% of patients with the disease. The test is negative in 90% of the patients without the disease. In light of this information what is the probability that your patient has Disease A? Disease Present (true) Absent (false) a c b Test Result Positive Negative d

14. 14 Disease Present Absent c a d b Test Result Positive Negative Sensitivity = a a + c Specificity = d b + d Base Rate = a + c (of disease a + b + c + d In population) Probability = a (Disease / a + b Positive test)

15. 15 Bayes theorem - formula for revising beliefs in light of new information. New odds for some event are: (1) the old odds (base rate) multiplied by (2) odds associated with the new information P (Disease / Positive Test) = Base Rate of Disease x Sensitivity of Test Overall P of a Positive Test OR a a + c P (D / PT) = a + c x a + b a + b + c + d a + b + c + d OR …… or more simply ……positive predictive value P (D / PT) = a a+b

16. 16 To Return to the Brazilian Rain Forest …………. Disease Present Absent Test Result Positive Negative P ( Disease / Positive Test ) = a = a + b 9 ≈ 1% 9 + 999 9 1 999 8991 b dc a

17. 17 SO ……….. Bayes’ Theorem takes account of: * Base rate information (disease prevalence) * Diagnosticity of tests (sensitivity / specificity) Bayes’ Theorem can also be used to: * Integrate information from multiple tests * Estimate the probability of one disease versus another Most important - Take home message Provides a way to think about importance of disease prevalence and sensitivity / specificity of tests in estimating the diagnostic utility of a test. -- USE TABLES

18. 18 Decision Making People far from perfect decision-makers. Limited by information available, amount of information that can be processed, and ability to combine information. However, often do very well. Important to realize when our judgments may be biased so we can try to take bias into account or rely on additional help.

19. 19 Readings and Key Terms Bernstein et al., Chapter 8, 288-291 Cognitive heuristicsAvailability Framing effects Bias: overconfidence, hindsight, confirmation Bayes’ Theorem -- positive predictive value