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We hope you have enjoyed this limited-length demo
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- View the Talks
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1. Adaptive clinical trials: overview 1
- Prof. Yu Shyr
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2. Adaptive clinical trials: overview 2
- Prof. Yu Shyr
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3. Bayesian adaptive designs for clinical trials
- Prof. Benjamin Saville
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4. Adaptive clinical trial design: randomization
- Prof. Hao Liu
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5. Adaptive designs for phase I trials 1
- Prof. Anastasia Ivanova
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6. Adaptive designs for phase I trials 2
- Prof. Anastasia Ivanova
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7. Case studies of adaptive early phase trials
- Prof. Daniel Normolle
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8. Phase II clinical trials - traditional approaches
- Prof. Fei Ye
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9. Phase II clinical trials - Bayesian methods
- Prof. Fei Ye
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10. Seamless phase II/III trials
- Prof. Elizabeth Garrett-Mayer
- Mr. Nathaniel O’Connell
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11. Frequentist approaches: sample size in adaptive clinical designs
- Prof. Tatsuki Koyama
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14. Ethical issues in adaptive clinical trials
- Dr. Spencer Phillips Hey
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15. Implementation of adaptive methods in early phase clinical trials
- Prof. Gina Petroni
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16. Design early phase drug combination trials: methods
- Prof. Ying Yuan
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17. Design early phase drug combination trials: software
- Prof. Ying Yuan
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18. Adaptation in likelihood trials
- Prof. Jeffrey Blume
Printable Handouts
Navigable Slide Index
- Introduction
- Continual Reassessment Method (CRM)
- CRM, how to compute the next dose?
- Selecting working model (dose-finding CRM)
- CRM: using the working model with data
- CRM: calculating posterior mean
- CRM: calculating posterior mean after 3 patients
- CRM: posterior mean & no dose limiting toxicity
- CRM: dose level for next patient
- Illustrating the difference between 3+3 and CRM
- More about the CRM model
- Example of a CRM trial
- Example of a CRM trial (graph)
- CRM as the number of subjects goes to infinity
- Illustration on non-convergence of the CRM
- Advantages of the CRM
- Disadvantages of the CRM
- Delayed outcome
- Mitigating uncertainty from patients still in follow-up
- Phase I trials with two agents: contour of doses
- Phase I trials with two agents: combination path
- Phase I trials with ordered groups (problem)
- Phase I trials with ordered groups (solution)
- References (1)
- References (2)
- References (3)
Topics Covered
- Continual Reassessment Method (CRM)
- Dose-finding designs for trials with delayed outcome
- Designs for dose-finding trials with multiple agents & ordered subpopulations
Links
Series:
Categories:
Talk Citation
Ivanova, A. (2016, September 30). Adaptive designs for phase I trials 2 [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 21, 2024, from https://doi.org/10.69645/INIM9889.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Anastasia Ivanova has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Adaptive designs for phase I trials 2
Published on September 30, 2016
22 min
A selection of talks on Pharmaceutical Sciences
Transcript
Please wait while the transcript is being prepared...
0:04
The Continual Reassessment Method (CRM)
was published in 1990.
It is the most frequently used
model-based design in oncology.
The assignment of doses
is very similar to the up-and-down designs,
in the sense that we can escalate,
deescalate or repeat the dose
for the next cohort of patients.
Patients can be assigned
one at a time or in groups.
We need to specify the total sample size.
It's usually 18-24,
if we have maybe four dose levels or five.
The dose assignment for the next cohort
or the next patient is determined based
on a parsimonious model.
It is also referred to as the working model.
In the initial escalation,
sometimes this working model
tells us to assign high doses right away.
However, because of ethical constraints,
we do not skip doses
in the initial escalation.
Escalation starts
at the lowest dose level
and then it goes to dose level
2, 3, and 4, etc...
1:26
So consider an example
where we have six dose levels selected,
what is our CRM working model?
There are several ways to specify the model.
Here, I will consider the so-called
'power model' as in the original 1990 paper.
This power model can be specified
as a constant for each dose level.
So we need six constants.
So these constants
should be specified in advance.
And then the model for toxicity probability
is such that the probability of toxicity,
given the parameter "a"
that is estimated, is equal to the constant
corresponding to a dose to the power of "a".
One can use the maximum
likelihood methods to estimate "a",
or one can use the Bayesian methods.
If we use a Bayesian variant of the CRM,
then we need to specify
the prior for the parameter "a".
For example, for this power model,
we can specify the exponential prior for "a."