, 7 tweets, 4 min read Read on Twitter
Today I'm @US_FDA w/superstar heart failure specialists, industry experts, patients, regulatory science experts, statistical experts; exploring new approaches to drawing inferences from clinical trial data. Can we do better than the traditional approach? Probably.
@US_FDA Conversations about TOPCAT, study of spironolactone for patients with heart failure and preserved ejection fraction. ~10 cents a day. Reasons to believe the drug is effective. Likely Russian & Georgian sites contaminated the trial. It is not strongly endorsed in the guidelines.
@US_FDA This is imp because no good treatments for these patients. And if you exclude Russia & Georgia, this trial is positive. And strong reasons to believe those sites enrolled people w/o the condition. Cheap drug. No profit motive. So no group pushing for re-examination of data.
@US_FDA This is about heterogeneity of effect, but in this case it derives from concerns about the integrity of some sites; the question is do we favor an analysis that assumes homogeneity of effects, even with reasons to think otherwise because of site behavior. @mfiuzat
@US_FDA @mfiuzat It is recurrent issue of subgroup analyses. Are we stuck with overall results even with knowledge that some regions may have contaminated the trial. How much do we discount the evidence? Meanwhile HF specialists in the room all believe, based on the trial, that the drug works.
@US_FDA @mfiuzat Another issue being raised… interpretability of the statistics for patients. How do we translate effects… and uncertainty? Not just about the math…but about the translation to practice. How do we inform decisions? @MinnowWalsh @coconnormd
@US_FDA Hearing about Spironolactone Initiation Registry Randomized Interventional Trial in Heart Failure W/Preserved EF (SPIRRIT). Based on registry in Sweden & large simple design in US. Enrollment in US is slow. We need to change our trial culture and capability in US. @mfiuzat
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