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Evaluating Poolability of Continuous and Binary Endpoints Across Centers Roseann White Clin/Reg Felllow Abbott Vascular – Cardiac Therapies 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 2 Evaluating Poolability across Centers • Key Issues • Current Methods • Proposed alternative method • Potential Bayesian approach Evaluating Poolability 09/29/2006 Company Confidential © 2006 Abbott 3 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 4 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 5 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: Yijk Ti (T * Center)ij ijk • 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 6 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 7 Proposed alternative Process • Prospectively define what a clinical meaningful interaction Measure Site 1 Site 2 • Determine sample size necessary to detect difference Evaluating Poolability 09/29/2006 Company Confidential © 2006 Abbott 8 Proposed Alternative Process (con’t) • Combine smaller centers (where enrollment is too low to detect differences) with larger “similar” centers where simliar is prespecified – 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 9 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 10 Bootstrap • Use bootstrap when – Actual possible group size is lower than needed – Actual possible group size is greater than needed • Simulation results N 1000 • Actual Grouping Size 25 Needed Grouping Size 150 % p<0.05 50 150 0.09 100 150 0.054 0.15 Limitations – For Binary outcomes, grouping sizes less than 50 can lead to misleading results Evaluating Poolability 09/29/2006 Company Confidential © 2006 Abbott 11 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. 2004 May;14(2):291-300. 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 12 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 13