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UID:20250813T230003EDT-1065PR4gAD@132.216.98.100
DTSTAMP:20250814T030003Z
DESCRIPTION:Breaking the Myth of Breaking Randomization: a Causal Examinati
 on of Arm-Based Meta-Analysis.\n\nIn the analysis of multi-arm randomized 
 trials\, methods for pooling data across trials generally belong to one of
  two broad classes. The first class of methods consists of contrast-based 
 estimators that estimate the contrast in treatment effect for each pair of
  treatment levels and then pool across the estimated contrasts. The second
  class encompasses arm-based methods that construct marginal estimates for
  each treatment arm and then computes the contrast from the marginal estim
 ates. Leading researchers have assailed arm-based methods under the broad 
 criticism of 'breaking randomization'\, implying biased estimation for pop
 ulation causal effects of treatment. However\, to date no one has establis
 hed a formal causal definition of 'breaking randomization'\, nor a critica
 l examination of the amount of bias that would result. In this talk\, I ch
 aracterize the conditions under which the bias of arm-based methods will (
 and will not) be biased for population causal effects and also discuss the
  advantages that arm-based methods have over contrast-based methods with r
 egards to precision.\n
DTSTART:20171114T203000Z
DTEND:20171114T213000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Russell Steele\, 9IÖÆ×÷³§Ãâ·Ñ
URL:/mathstat/channels/event/russell-steele-mcgill-282
 665
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