Registration for a live webinar on 'Precision medicine treatment for anticancer drug resistance' is now open.
See webinar detailsWe noted you are experiencing viewing problems
-
Check with your IT department that JWPlatform, JWPlayer and Amazon AWS & CloudFront are not being blocked by your network. The relevant domains are *.jwplatform.com, *.jwpsrv.com, *.jwpcdn.com, jwpltx.com, jwpsrv.a.ssl.fastly.net, *.amazonaws.com and *.cloudfront.net. The relevant ports are 80 and 443.
-
Check the following talk links to see which ones work correctly:
Auto Mode
HTTP Progressive Download Send us your results from the above test links at access@hstalks.com and we will contact you with further advice on troubleshooting your viewing problems. -
No luck yet? More tips for troubleshooting viewing issues
-
Contact HST Support access@hstalks.com
-
Please review our troubleshooting guide for tips and advice on resolving your viewing problems.
-
For additional help, please don't hesitate to contact HST support access@hstalks.com
We hope you have enjoyed this limited-length demo
This is a limited length demo talk; you may
login or
review methods of
obtaining more access.
- View the Talks
-
1. Adaptive clinical trials: overview 1
- Prof. Yu Shyr
-
2. Adaptive clinical trials: overview 2
- Prof. Yu Shyr
-
3. Bayesian adaptive designs for clinical trials
- Prof. Benjamin Saville
-
4. Adaptive clinical trial design: randomization
- Prof. Hao Liu
-
5. Adaptive designs for phase I trials 1
- Prof. Anastasia Ivanova
-
6. Adaptive designs for phase I trials 2
- Prof. Anastasia Ivanova
-
7. Case studies of adaptive early phase trials
- Prof. Daniel Normolle
-
8. Phase II clinical trials - traditional approaches
- Prof. Fei Ye
-
9. Phase II clinical trials - Bayesian methods
- Prof. Fei Ye
-
10. Seamless phase II/III trials
- Prof. Elizabeth Garrett-Mayer
- Mr. Nathaniel O’Connell
-
11. Frequentist approaches: sample size in adaptive clinical designs
- Prof. Tatsuki Koyama
-
14. Ethical issues in adaptive clinical trials
- Dr. Spencer Phillips Hey
-
15. Implementation of adaptive methods in early phase clinical trials
- Prof. Gina Petroni
-
16. Design early phase drug combination trials: methods
- Prof. Ying Yuan
-
17. Design early phase drug combination trials: software
- Prof. Ying Yuan
-
18. Adaptation in likelihood trials
- Prof. Jeffrey Blume
Printable Handouts
Navigable Slide Index
- Introduction
- Common trial: how do I get to work?
- Common trial: solution
- Common trial: adaptive aspects
- Traditional drug development
- Traditional approach challenges: Phase II
- Phase II solution: adaptively randomize
- Traditional approach challenges: Phase III
- Phase III Solution: adaptive sample size
- How are clinical trials similar to missiles?
- Clinical trials as missiles
- Adaptive clinical trials
- Adaptation
- Example of response adaptive randomization
- Research question
- Comparative effectiveness
- Trial overview
- Bayesian adaptive design features
- Adaptive allocation
- Early stopping
- Example trial: 300 pt analysis
- Example trial: 400 pt analysis
- Example trial: 500 pt analysis
- Example trial: 600 pt analysis
- Example trial: final evaluation
- Comparison to non-adaptive randomization
- Summary: response adaptive randomization
- Desirable qualities of an RCT
- Why are study designs (usually) fixed?
- Why adapt? prospective postmortem
- Why adapt?
- Equipoise
- FDA critical path initiative
- Spending more, getting less
- Critical path initiative
- Typical adaptations (1)
- Adaptations: Stopping at the right time
- Sample size selection
- Goldilocks design: decision rules
- Typical adaptations (2)
- Typical prospective adaptive design
- Mapping an adaptive trial
- When is adaptation most valuable?
- Drawbacks of adaptation (1)
- Drawbacks of adaptation (2)
- Who to involve?
- Acceptability to key stakeholders
- Is now a prime time for adaptive designs?
- Time is right for adaptive designs
- FDA guidance documents
- Online tools & resources
- Some current areas of application
Topics Covered
- Challenges of traditional clinical trial approaches
- Adaptive clinical trial characteristics
- Examples of adaptive trials
- Why use adaptive trials
- Drawbacks of adaptive trials
- Bodies that need to be involved in adaptive trials
- The time is right for adaptive designs
- Resources & current applications
Talk Citation
Saville, B. (2016, December 29). Bayesian adaptive designs for clinical trials [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 21, 2024, from https://doi.org/10.69645/WEWE9582.Export Citation (RIS)
Publication History
Financial Disclosures
- Professor Benjamin Saville has no commercial/financial relationships to disclose
A selection of talks on Pharmaceutical Sciences
Transcript
Please wait while the transcript is being prepared...
0:00
My name is Ben Saville.
I'm a statistical scientist
from Berry Consultants.
I'm also an adjunct
assistant professor
at the Vanderbilt University School
of Medicine in Nashville, Tennessee.
And this is a talk on Bayesian
Adaptive Designs for Clinical Trials.
0:16
Many of us have probably
moved to a new place
or a new city and started a new job,
and you might be posed with
the question of how do you get to work
and you know there are
different options, of course.
0:31
You could take a highway,
you could take the back roads,
you have to figure out
which way you're going to go.
You want to go the optimal way
to get you to work as fast as you can
and without stressing you out too much.
There are different solutions
to decide how to do this.
One possible solution
which I'm sure we've all done
is you take 30 envelopes
in your car with you
and it has one of three routes on it
and you randomly select an envelope
and you drive that route.
Record the drive time and at the end
of the 30 days you look at your data,
that point you drive whichever route
had the fastest average drive time.
I'm sure we've all done that
and I'm being facetious here
because obviously that's something
that probably no one would do.
The more intuitive way
that mostly all of us
would decide how to drive to work
would be to pick one route,
you drive it the first day,
you make a note of how long it took you
and then maybe the next day
you drive a different way.
And if it took you longer
than the first day
then maybe you switch back to the first
and we iterate through this process
of trying different ways.
1:31
Really, we tend to do more
of an adaptive approach
to figuring out
which way to drive to work.
If one route takes too long,
we're probably less
likely to try it again
and if we take it twice
and it takes a long time both times,
then we're probably going to drop it.
If you drive a certain way one way
and it takes you forever, you may
never even go that way again.
So basically, the idea here
is that there's a point
at which you feel like
you have enough data
to convince you that the one route
is faster than the other.