1. Cognitive Bias Regarding Risks and Benefits
Brian Robinson MSc PhD
Senior Lecturer,
Graduate School of Nursing, Midwifery & Health
Victoria University of Wellington

2. How do we make decisions around the benefits and harms of clinical interventions when we are uncertain of any possible outcome?

3. P<0.05 Odds Ratio SEM SD Harm Ratio DALY Numbers Needed to Treat
Meta-analysis
primary outcome
Confidence Interval
Specificity
Sample Size
ANOVA
mode
numbers needed to screen
clustering
t-test
SEM
estimation
Odds Ratio
regression
selectivity
Power
P<0.05
Relative Risk
Randomised
Normal Distribution
Numbers Needed to Treat
likelihood
weighting
absolute risk SD
students-test
mean
ANCOVA
R2>0.80
correlation
repeatability
F-test
difference
Sensitivity
DALY
Harm Ratio
LLY
inference
Chi-squared test
median
covariate
secondary outcome
Survival Rate
Non-inferiority test
Confidence interval
non-parametric analysis

4. Statistical Illiteracy
Common to patients, journalists, and physicians
This is created by
nontransparent framing of information
unintentional lack of understanding
intentional efforts to manipulate or persuade
Gigerenzer, G, Gaissmaier, W, Kurz-Milcke, E, Schwartz, LM. and Woloshin, S Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8:

5. Estimating Benefit and Harm in Treatments, Tests and Screening
“Participants [the public] rarely had accurate expectations of benefits and harms, and for many interventions, regardless of whether a treatment, test, or screen, they had a tendency to overestimate its benefits and underestimate its harms”
Hoffman, TC & Del Mar, C Patients’ expectations of the benefits and harms of treatments, screening, and tests. JAMA 175:

6. Estimating Benefit and Harm in Treatments, Tests and Screening
“Clinicians themselves may have overly optimistic expectations about the benefits of interventions and poor knowledge of harms and may oversell interventions when offering them to patients.”
Hoffman & Del Mar (2015)

7. Making Rational Decisions
Kahneman, D. (2011) Thinking, fast and slow. Farrar, Straus and Giroux. New York.

8. The Certainty Principle
Would you prefer- or
50% chance to win a 3-week tour of England, France, and Italy
B.(100% certain) 1-week tour of England
C.5% chance to win a 3-week tour of England, France, and Italy
D.10% chance to win a 1-week tour of England or
The majority of people will pick B (78%) over A (22%), but choose C (67%) over D (33%)
Kahneman, D & Tversky, A Prospect theory: an analysis of decision under risk. Econometrica, 47:

9. Framing Outcomes: Problem One
Imagine that you have decided to see a play where the admission is $10 per ticket. As you enter the theatre you discover that you have lost a $10 bill. Would you still pay $10 for a ticket for the play?
Yes: 88% No: 12%
Tversky, A & Kahneman, D The framing of decisions and the psychology of choice. Science. 211:

10. Framing Outcomes: Problem Two
Imagine that you have decided to see a play and paid the admission that is $10 per ticket. As you enter the theatre you discover that you have lost the ticket. The seat was not marked and the ticket cannot be recovered. Would you pay $10 for another ticket?
Yes: 46% No: 54%
Tversky & Kahneman (1981)

11. Value Function of Prospect Theory+
S-shape curve represents diminishing sensitivity for gains and losses
Curves are not symmetrical: response to losses is stronger than response to gains
-$200
-$100
LOSSES
GAINS
$100
$200
PSYCHOLOGICAL VALUE -
Kahneman (2011)

12. Probability Discounting with Gains and Losses
1.0
Certain loss-uncertain gain
0.8
Accepting the certain loss of zł1 to zł899 with a 95%, 70%, 30% or 5% chance to gain zł900
0.6
0.4
Accepting the certain loss of zł10 to zł8990 with a 95%, 70%, 30% or 5% chance to gain zł9000
0.2
Subjective Value (proportion)
0.0
Certain gain-uncertain loss
-0.2
Accepting the certain gain of zł1 to zł899 with a 95%, 70%, 30% or 5% chance to lose zł900
-0.4
Accepting the certain gain of zł1 to zł8990 with a 95%, 70%, 30% or 5% chance to lose zł9000
-0.6
-0.8
-1.0
95%
70%
30%
Probability 5%
Ostaszewski, P and Bialaszek, W Probabilistic discounting in “certain gain–uncertain loss” and “certain loss–uncertain gain” conditions. Behavioural Processes, 83,

13. Probability Discounting and Cardiovascular Risk
Finds the smallest benefit a drug must provide to tolerate its side effect*
Untreated:
30 of 100
Treated:
15 of 100
Side effect
Reject treatment
Accept treatment
18 of 100
12 of 100
Illness Frame: Number of people who will experience a heart attack or stroke while on Drug X for 5 years
Untreated:
70 of 100
Treated:
85 of 100
Side effect
82 of 100
88 of 100
Accept treatment
Reject treatment
Health Frame: Number of people who will have a healthy heart and circulation while on Drug X for 5 years
*Side effect: either “frequent headache” or “cold hands and feet”
Asgarova, R., Macaskill, A.C., Robinson, B.J. and Hunt, M.J., Probability discounting and cardiovascular risk: the effect of side-effect severity and framing. The Psychological Record, 67,

14. Probability Discounting and Cardiovascular Risk
Asgarova, R, Macaskill, AC, Robinson, BJ & Hunt, MJ.,2017. Probability discounting and cardiovascular risk: the effect of side-effect severity and framing. The Psychological Record, 67,

15. Implications for Patient Care
Statistical methods, analyses and results are unintuitive and difficult for physicians and patients to understand
It is natural to make intuitive or irrational decisions that overestimate benefit and underestimate harm
Framing information about outcomes affects decisions
Responsibility exists with organisations and clinicians to monitor communication affecting patients’ decisions that impact on their health and wellbeing