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CALSCALE:GREGORIAN
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BEGIN:VEVENT
DTSTAMP:20260408T081318Z
DTSTART;TZID=America/New_York:20230929T033000
DTEND;TZID=America/New_York:20230929T053000
SUMMARY:Epistemic Diversity\, Ethics\, and the Optimal Timing of Clinical Trials
UID:20260408T105410Z-iCalPlugin-Grails@philevents-web-f5d4878dd-r5qzs
TZID:America/New_York
LOCATION:4200 Fifth Avenue\, Pittsburgh\, United States\, 15213
DESCRIPTION:<p>ALS - Alex John London <br> <br>Title:&nbsp\;Epistemic Diversity\, Ethics\, and the Optimal Timing of Clinical Trials</p>\n<p>Abstract:</p>\n<p>Ethically acceptable research with human participants should satisfy at least two ethical criteria: it should produce sufficient social value to justify its conduct and it should respect the basic rights and interests of study participants.&nbsp\; The concept of clinical equipoise has risen to prominence because it purports to reconcile these objectives by connecting the conditions for initiating and terminating a trial to a state of uncertainty within the relevant community of experts.&nbsp\; In this talk I present a method for representing the epistemic state of a community of experts and I show that distributions of expert assessment that are likely to be regarded as canonical examples of equipoise (especially states that reflect some notion of &ldquo\;equal&rdquo\; distribution) fail to satisfy one or more of the above ethical criteria.&nbsp\; I consider distributions of expert assessment that reflect healthy vs unhealthy epistemic diversity and draw some general conclusions about the optimal timing of clinical trials. &nbsp\;Time permitting\, I will show how these results provide further support for conceptualizing trials that use response adaptative randomization as modeling the dynamical change in the distribution of assessments among different experts in a community rather than as the dynamics of belief change of some group agent or meta-expert.</p>\n<p>This talk will also be available live streamed on YouTube at&nbsp\;https://www.youtube.com/channel/UCrRp47ZMXD7NXO3a9Gyh2sg.</p>\n
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