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1. Introduction
2. Course outline
3. Synopsis (part 2)
4. Prior information
5. Increasing the data strength
6. Decreasing the prior strength
7. Weak prior formulations
8. Uniformity is untenable
9. Many weak priors
10. When should we use weak priors?
11. Informative priors
12. Elicitation
13. The questions influence the answers: example (1)
14. The questions influence the answers: example (2)
15. Heuristics and biases
16. What to ask for in elicitation?
17. Probabilities
18. Fitting a distribution
19. Elicitation: case study
20. Bayesian clinical trial simulation
21. Distributions elicited
22. Elicitation record
23. Eliciting one distribution
24. Multivariate elicitation
25. Example: patient level IPA
26. Hierarchical modelling
27. Resources on elicitation
28. Concluding remarks

Topics Covered

• An important component of the Bayesian approach is its use of prior information in addition to the data
• Requires the formulation of prior information as a prior probability distribution
• Ways of specifying prior distributions
• Weak prior distributions
• Informative prior distributions
• Elicitation of expert knowledge and hierarchical modeling
• Psychological biases to be avoided
• Good elicitation practice
• Case study in drug development

Links

Series:

• Bayesian Methods in Health Economics

Categories:

• Methods

Talk Citation

Talk Information

• Published on February 16, 2009