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

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

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

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. 2007. Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8: 53-96.

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. 2015. Patients’ expectations of the benefits and harms of treatments, screening, and tests. JAMA 175: 274-286.

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)

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

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. 1979. Prospect theory: an analysis of decision under risk. Econometrica, 47: 263-291.

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. 1981. The framing of decisions and the psychology of choice. Science. 211: 453-458.

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)

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)

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. 2010. Probabilistic discounting in “certain gain–uncertain loss” and “certain loss–uncertain gain” conditions. Behavioural Processes, 83, 344-348.

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., 2017. Probability discounting and cardiovascular risk: the effect of side-effect severity and framing. The Psychological Record, 67,169-179

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, 67,169-179.

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