1. Roseann White Clin/Reg Felllow Abbott Vascular – Cardiac Therapies
Evaluating Poolability of Continuous and Binary Endpoints Across Centers
Roseann White
Clin/Reg Felllow
Abbott Vascular – Cardiac Therapies

2. Background MA in Statistics from UC Berkeley
2nd Generation statistician (My mother specialized in econometrics, i.e. statistics for economics)
15 years as professional statistician in the Biotechnology industry providing statistical support for research, analytical method development, diagnostics, clinical, and manufacturing
Favorite Quote:
“Statisticians speak for the data when the data can’t speak for themselves”
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

3. Evaluating Poolability across Centers
Key Issues
Current Methods
Proposed alternative method
Potential Bayesian approach
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

4. Key Issues Centers are not chosen at random
Sponsors try to include centers that will represent the patient population across the geography
Often there are no centers or centers that receive very few of the type of patients needed
Clinical Trials tend to initiate more centers than they potentially need
Accelerate enrollment
Involve Key Opinion leaders
Provide visibility to product
In device trials, its often difficult to “blind” the clinician to the product being used.
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

5. Key Issues
Assessing poolability is rarely discussed prospectively from both a clinical as well as statistical perspective
No definition of what a clinical meaningful difference among sites
When assessing poolability for centers with a small number of patients, should they be combined
Based on size of center?
Based on what geographical region the center is located?
Based on standard practices?
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

6. Current Methodology
Centers that have less than a pre-specified number of patients are combined into a “center”
The interaction effect between center and treatment is tested:
If the p-value is greater than a pre-specified value, then there is evidence of the lack of poolability of the sites
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

7. Challenges to the current methodology
Reflexive – does not take into consideration whether a clinically meaningful interaction would be detected
Combination of all the smaller sites may dilute regional differences
What p-value does one choose?
<0.05 to only pick up extreme differences. i.e. increase specificity and decrease sensitivity
>0.05 so to increase the sensitivity but decrease the specificity
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

8. Proposed alternative Process
Prospectively define what a clinical meaningful interaction
Determine sample size necessary to detect difference
Measure
Site 1
Site 2
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

9. Proposed Alternative Process (con’t)
Combine smaller centers (where enrollment is too low to detect differences) with larger “similar” centers where simliar is pre-specified
Geographical similar (same country or region)
Same patient population (urban vs. rural)
Same standard practices (con-committment medication use)
If center groupings are still too small – use bootstrap method of resampling to get the “appropriate number” from each site
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

10. Example – Binary Endpoint
Primary endpoint – non-inferiority in oputcome rate
Assumptions T1=9%, T2=9%, margin=5%
N=1400
Clinical Meaningful interaction between treatment groups:
If the difference between the treatment group varies more than twice the non-inferiority margin
Minimum grouping size=150;
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

11. Bootstrap N Actual Grouping Size Needed Grouping Size % p<0.05 1000
Use bootstrap when
Actual possible group size is lower than needed
Actual possible group size is greater than needed
Simulation results
Limitations
For Binary outcomes, grouping sizes less than 50 can lead to misleading results N
Actual Grouping Size
Needed Grouping Size
% p<0.05
1000 25
150
0.15 50
0.09
100
0.054
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

12. Does Bayesian play a role in determining poolability?
Modify the approach presented by Jen-Pei Liu, et. al. in “A Bayesian noninferiority approach to evaluation of bridging studies” J Biopharm Stat May;14(2):
Step 1: Develop a prior based on the treatment difference based on largest center grouping
Step 2: Use the data from the next largest center grouping and prior distribution to obtain the mean and variability of the posterior distribution
Step 3: Evaluate the posterior probability that difference is greater or equal to some clinically acceptable limit
Step 4: If the posterior probability is sufficiently large, say 80%, then conclude the similarity between the two center groupings.
Step 5: Repeat the same process with next center grouping.
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott

13. Conclusion Pre-specify clinical meaningful difference up front
Group smaller sites by commonalities not size
If group size is smaller or much larger than needed – potential solution is using bootstrap but need more investigation
Explore bayesian approach to evaluating poolability.
Evaluating Poolability
09/29/2006
Company Confidential © 2006 Abbott