What’s Beyond Poolability: Exchangability Roseann White, MA Director Pragmatic Clinical Trial Statistics Duke Clinical Research Institute

Acknowledgements Dawn Bardot, VP Tecnologies, MDIC Dan Schwartz, PM, MDIC Tarek Haddad, Ph.D., Director, MDT

Disclosure Statement of Financial Interest Within the past 12 months, I or my spouse/partner have had a financial interest/arrangement or affiliation with the organization(s) listed below. Affiliation/Financial Relationship Company Employee Consulting Fees/Honoraria Other Financial Benefit Duke Clinical Research Institute Claret Abbott, Abbvie

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Exchangability: outcomes from persons from two sources can be explained by deterministic characteristics and random error UNC and Duke University Basketball Teams are exchangeable 2015 UNC won 33 out of 40 games, i.e. 85% 2015 Duke University won 25 out of 36 games, i.e. 70%

What do we want to look at to assess exchangeability Trial Design: inclusion/exclusion criteria, primary endpoint, …….. Geography Device Definitions of the outcomes, key baseline characteristics,……. Quality of the Data: monitoring level , adjudication, ……. Follow-up: same schedule of follow-up visits, same kind of follow-up visits, ……. Your statistician can assist but the clinician/clinical trial scientist is the final judge of exchangability

Completely Exchangeable  Completely Separate

Comparing Historical Data to your Current Data 100% Completely Exchangeable (poolable): any differences between in outcomes between the can be adjusted for by deterministic characteristics Partial/Conditional: average outcomes for persons from historical and current data, after adjusting for deterministic characteristics, arise from a common distribution Completely Separate: we ignore the historical information except in how it might help us understand designing a study from “scratch” 0%

Why do we want to know exchangability? Historical Historical Historical Current Current Current

To determine the discount function one needs to balance: Creating the Discount Function: Collaborative Effort between Statistician and Clinical Scientist To determine the discount function one needs to balance: Type I error: False Hope Type II error: Missed opportunity Bias: Smallest clinical meaningful shift in outcomes

“Past is prologue” William Shakepeare (a literary Bayesian)