A National unit for Bayesian Health Decision Science

Data Evidence Decision

Data Evidence Decision

Data Evidence Decision

Data Evidence Decision

Decision science is the study of how people make decisions and how they can make better decisions in the presence of uncertainty, complexity and competing values.

Classical Statistics …

© 1908

Data Null Hypothesis Theoretical Distribution p-value

If the p-value is < 0.05, then we reject the null hypothesis, and say that there is a ‘treatment effect.’ Otherwise, we have insufficient evidence to be able to claim a ‘statistically significant’ finding.

If the drug is safe and there is a statistically significant treatment benefit, then licence the drug. Otherwise, don’t grant it a licence. ‘Traditional’ new drug decision.

Which would you recommend … Treatment (A) or (B)? An RCT published in NEJM shows that (A) works better … (p=0.013) More Important (?) Concerns …

Which would you recommend … Treatment (A) or (B)? An RCT published in NEJM shows that (A) works better … (p=0.013) OR = 6.0 (95% CI 1.5 – 24.7) More Important (?) Concerns …

Which would you recommend … Treatment (A) or (B)? … given that … … treatment with (A) costs €600 compared to €30 and requires 2 months off work. The condition in question is ingrown toenail. The outcome is recurrence free at 1 year. More Important (?) Concerns …

Which would you recommend … Treatment (A) or (B)? … given that … … treatment with (A) costs €600 compared to €30 and requires 2 months off work. The condition in question is a congenital heart defect. The outcome is survival after 5 years. More Important (?) Concerns …

A Bayesian world …

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Bayes is different … You don’t (and will never) know anything … ‘hold onto your uncertainty’. Everything you have experienced counts when doing inference (prior belief). Values (as utilities) are naturally a part of the analytical framework. Sequential (or complex) decision problems can be dealt with coherently.

21 Results of experiment What I believed before I did the experiment Probable values for the outcome of interest

… Bayes theorem only tells us of how to update our beliefs (in probabilistic terms) … it does not tell us how to decide whether something is statistically significant or otherwise … we can use the same ‘rule’ as the 0.05 – but we can do otherwise – utility is a natural fit …

Utility = 0.5 Utility = 0 Utility = 1 Ingrown toenail … Surgery Ointment p=0.6

Utility = 0.5 Utility = 0 Utility = 1 Ingrown toenail … Surgery Ointment p=0.6 Decision Node

Utility = 0.5 Utility = 0 Utility = 1 Ingrown toenail … Surgery Ointment p=0.6 Chance Node

Utility = 0.5 Utility = 0 Utility = 1 Ingrown toenail … Surgery Ointment p=0.6 Value of the Outcome

Maximise subjective expected utility. That is, consider the courses of action that are available. If there are some uncertain parameters, use the probability distributions. Choose the path that leads to the best outcome ‘on average.’ Bayesian decision-making

Tx A Tx B Utility of (Failure of A combined with costs etc) Utility of (Success of A combined with costs in financial, time and side effects) Utility of (Success of B combined with costs etc) Utility of (Failure of B combined with costs etc)

Bayesian decision-making Tx A Tx B U(A+,-costs) U(A-,-costs) U(B-,-costs) U(B+,-costs)

Bayesian decision-making Tx A Tx B U(A+,-costs) U(A-,-costs) U(B-,-costs) U(B+,-costs) P(B+|Data) P(A+|Data)

Bayesian decision-making Tx A Tx B U(A+,-costs) U(A-,-costs) U(B-,-costs) U(B+,-costs) Expected Utility(A|Data) Expected Utility(B|Data) Expected Utility = sum(P*U) choose the biggest

Bayesian decision-making Tx A Tx B U(A+,-costs) U(A-,-costs) U(B-,-costs) U(B+,-costs) P(B+|Data) P(A+|Data)

OR = 6.0 (95% CI 1.5 – 24.7) Treatment with (A) costs €600 compared to €30 and requires 2 months off work. The condition in question is a congenital heart defect. The outcome is survival after 5 years. The condition in question is ingrown toenail. The outcome is recurrence free at 1 year. More Important (?) … revisited

Health Technology Assessment

Motivation Health technology assessment (HTA) is a structured methodology, helping identify which technologies or pharmaceutical agents should have priority in being introduced into the health system. This contrasts with regulatory concerns, which are designed to ensure ‘fitness for purpose’.

Tx 1 (Sustained) Tx 2 (+efficacy, waning.) Effectiveness = Long term overall benefit

Decision Making - the C/E plane The cost effectiveness plane is a core aspect of how outcomes are communicated and interpreted. It trades off gains in health outcomes (on the x-axis) and costs (on the y-axis). Note that it is only a part of the process and only ‘informs’ decision.

Simple Decisions... ? (Q,C) Do It ++ Health ++ Investment

Simple Decisions... ? (Q,C) Don’t do It

Simple Decisions... ? (Q,C) Put it on the list of things to do …

Simple Decisions... ?

System Model InputOutput New Input Inference Costs, QALYs Progression rates, Efficacy etc.

System Model InputOutput New Input Inference Costs, QALYs Progression rates, Efficacy etc. Probability Distribution

System Model InputOutput New Input Inference Costs, QALYs Progression rates, Efficacy etc. Probability Distribution

Bayes + HTA A centre …

d 1, d 2, d 3,... (Exclusive and exhaustive collection of actions)

Information (about effects, costs etc)

The utility of the Decision Maker

Benefit according to Decision Maker. Schmitz et al.

Feedback weights to Decision Maker Yields estimates of coefficients of the criteria X

Calibration (with ScHARR & CHDS)

Single Study?

Bias Correct?

Multiple Studies?

Indirect Evidence?

Schmitz et al.

To do … MCDA … with models to identify weights for the decision criterion … then feedback loop to Decision Makers. Calibration … HPV, CRC, … Hep C and Alzheimer’s Disease. Evidence Synthesis … online functionality with IPD, associated covariates.