1. Assessing Similarity to Support Pediatric Extrapolation
Forrest Williamson
Research Scientist

2. Extrapolation The Bridge to Pediatric Efficacy from Adult Efficacy
Pediatric (or “target”) efficacy conclusion
1994: Final Regulation: Pediatric Labeling Rule
“A pediatric use statement may also be based on adequate and well-controlled studies in adults, provided that the agency concludes that the course of the disease and the drug’s effects are sufficiently similar in the pediatric and adult populations to permit extrapolation from the adult efficacy data to pediatric patients. Where needed, pharmacokinetic data to allow determination of an appropriate pediatric dosage, and additional pediatric safety information must also be submitted”
Efficacy may be extrapolated from adequate and well-controlled studies in adults to pediatric patients if:
The course of the disease is sufficiently similar
The response to therapy is sufficiently similar
Dosing cannot be fully extrapolated
Safety cannot be fully extrapolated
Response to therapy
Adult (or “source”) efficacy conclusion
Disease Progression

3. Challenges Finding Balance
Informative
Feasible
Efficacy
Safety
Sample Size
Fine balance between being informative and making it feasible. Too large a sample size may risk not being able to complete trials in children.
Need to balance scientific necessity and scientific validity.
“Need to minimize number of subjects enrolled in pediatric clinical trials and the need to maximize the usefulness of the data obtained, while ensuring that the trials are feasible, robust, and interpretable.” – Dunne et al. (2011)6

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Problem Statement
Generally, pediatric studies will have smaller sample sizes than adult studies in the same (or similar) indication
Low prevalence
Lack of willingness by physicians or caregivers to participate in clinical trials
Eligibility criteria for RCT too restrictive
SoC differences globally
Off-label use of therapies approved in adults
How do we handle this increased uncertainty, when extrapolation is (or may be) appropriate to some degree?
10/14/2019
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Outline of Methods
Bayesian borrowing
Outcome-driven
When available data exists outside the new study to support extrapolation, that data can be leveraged to reduce the uncertainty. Methods include: proportional discounting, mixture priors (including robust mixtures), modified power prior, commensurate priors, matching…and many more!
Rather than relying on strict inference, embrace the uncertainty and instead use outcome-based decision-theory to determine study success via the use of efficacy checks and consistency checks, based on available data to support extrapolation.
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Mixture Prior
𝜋 𝑀𝑃 𝜃,𝑤 𝐶 0 , 𝑆 0 = 1−𝑤 ∗𝜋 𝜃 𝑆 0 +𝑤∗𝜋(𝜃| 𝐶 0 )
𝑤=0.5
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Mixture Prior
The Bayesian updating will sample more heavily under the region of the prior that aligns with where the new data is observed (even for fixed values of the mixing weight, 𝑤).
When it’s not clear which group should be contributing more, the HPD region is larger around the observed response rate in the target population.
Discounting may still be applied to the marginal prior structures, if the impact of the borrowing is too heavy.
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Robust Mixture
Robust (vague) prior
Source data posterior
Mixture weight
𝑤=0
𝑤=0.25
𝑤=0.5
𝑤=0.75
𝑤=1
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Robust Mixture Prior
The impact of the informative prior component is dependent on how close the response rate in the target population is to that in the source population.
As the response rate in the target population drifts from that in the source population, the robust prior will contribute more to the analysis.
EHSS is a continuous function of drift (prior-data conflict).
Perhaps counter-intuitively to this application (extrapolation), when the pediatric response rate is much higher than that seen in adults, the HPD region will be wide again and could even fail to show lack of separation from the control.
Perhaps counter-intuitively for this application (extrapolation), when the pediatric response rate is much higher than that seen in adults, the HPD region will grow wider (as drift increases) and could even fail to show lack of separation from the control group.
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Efficacy Check
Check that the response rate in the target population is better than the control response rate
Observed rate in the target population
UB of 90% CI on control response rate
“Extrapolation is appropriate, as long as new data supports that the effect is not control-like.” X
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Consistency Check
Check that the response rate in the target population is at least as good as the source population response rate
Observed rate in the target population
LB of 90% CI on source population response rate
“Extrapolation is appropriate, as long as new data supports that the effect is similar between the two populations.” X
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12. Consistency Check: Alternatives
Fixed Delta (within 6%)
Relative Effect (60% of effect)
A fixed delta from the observed Source response rate, e.g. within 6% of the response.
A percentage of the observed Source response rate, e.g. maintain at 60% response.
LB of CI and Fixed Delta may be similar, however the Fixed Delta is truly data-independent (other than where is will be centered).
Fixed Delta may be appropriate when there’s some margin of clinical meaningfulness between the populations.
Furthermore, the CI may be very narrow on the source population when a lot of data are available, meaning consistency will be harder to declare the more information you have on the source population.
Relative effect is nice because, perhaps counter-intuitively, the hurdle the meet the consistency check is higher the better the drug effect if using CI or Fixed methods. Relative is really driven by data and less punitive when the drug effect is good.
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13. Using Both Checks
Observed target response rate must be
greater than the upper bound of the 90% CI for the control, AND
greater than or equal to 60% of the observed response rate of the source population.
In practice, only one of these checks will be the limiting factor
argmax{efficacy check, consistency check}
It may not be known a priori which check will be more stringent
(it could rely on data not yet observed)
Using both checks together may improve operating characteristics:
↑ probability of true positive
↓ probability of false positive
Sensitivity analyses must be used to fully understand the importance of the efficacy and consistency criterion on any the unknown information from the control, source, and target populations.
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14. Example
Efficacy check: target response rate is greater than the upper bound of the 90% CI for the control.
Consistency check: target response rate is greater than or equal to 70% of the observed response rate of the source population.
Explore a range of possible responses
Sample size of 400 is assumed for the control and source populations, and sample size of 50 is used for the target population.
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15. Example Changing the consistency criterion
Lowering the consistency criterion
Raising the consistency criterion
Efficacy check is driving the outcome
Consistency check is driving the outcome
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Conclusions
Borrowing methods may be leveraged to reduce our uncertainty in the pediatric drug effect
Test-then-borrow requires a “line in the sand” approach
Mixtures allow for borrowing to occur from multiple possible states of truth, and the impact of the prior on the posterior is relative to the newly observed data (degree of borrowing is on a continuous scale)
Extrapolation requires similarity in response, an assumption which may need to be validated
Efficacy/Consistency checks do not have to rely on formal inference (although they may)
Operating characteristics may be simulated to specify decision criterion by optimizing operating characteristics in the same way inferential decision rules are selected
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