An overview of Bayesian adaptive clinical trials, what they could do well and what to look out for.

1. Bayesian adaptive clinical trials:
Promise and pitfalls
John D. Cook
March 30, 2016

2. Why adaptive clinical trials?
Human subjects not like light bulbs.

3. Why adaptive clinical trials?
Human subjects not like light bulbs.
Have to proceed cautiously.

4. Why adaptive clinical trials?
Human subjects not like light bulbs.
Have to proceed cautiously.
Want to treat subjects effectively.

5. Objectives
Maximize the probability/expectation of a good outcome

6. Objectives
Maximize the probability/expectation of a good outcome
Mimimize the probability/expectation of a bad outcome

7. Objectives
Maximize the probability/expectation of a good outcome
Mimimize the probability/expectation of a bad outcome
Stop early because things are looking bad

8. Objectives
Maximize the probability/expectation of a good outcome
Mimimize the probability/expectation of a bad outcome
Stop early because things are looking bad
Stop early because things are looking good (less common)

9. Why Bayesian?
Flexibility

10. Why Bayesian?
Flexibility
Sequential nature of Bayes rule

11. Why Bayesian?
Flexibility
Sequential nature of Bayes rule
Can incorporate prior information

12. Traditional designs
VERY crude, e.g. 3+3

13. Traditional designs
VERY crude, e.g. 3+3
Infrequent monitoring

14. Traditional designs
VERY crude, e.g. 3+3
Infrequent monitoring
Uses very little data

15. Opportunities for improvement
Use continuous outcomes

16. Opportunities for improvement
Use continuous outcomes
Monitor frequently or continuously

17. Opportunities for improvement
Use continuous outcomes
Monitor frequently or continuously
Use patient characteristics

18. Opportunities for improvement
Use continuous outcomes
Monitor frequently or continuously
Use patient characteristics
Use multiple outcome events

19. Dangers
Fitting complex models with little data

20. Dangers
Fitting complex models with little data
A priori overfitting

21. Dangers
Fitting complex models with little data
A priori overfitting
Exploring high dimensional design/outcome space:

22. Dangers
Fitting complex models with little data
A priori overfitting
Exploring high dimensional design/outcome space:
Arbitrary simulation scenarios

23. Dangers
Fitting complex models with little data
A priori overfitting
Exploring high dimensional design/outcome space:
Arbitrary simulation scenarios
May have blind spot

24. Dangers
Fitting complex models with little data
A priori overfitting
Exploring high dimensional design/outcome space:
Arbitrary simulation scenarios
May have blind spot
Temptation to cherry pick scenarios

25. Contact info