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DESCRIPTION:Harlan Campbell\, PhD\n\nPostdoctoral Research Fellow | Departm
 ent of Statistics | University of British Columbia\n\nWhere: Virtual | Zoo
 m\n\nAbstract\n\nEstimating the COVID-19 infection fatality rate (IFR) has
  proven to be particularly challenging –and rather controversial– due in l
 arge part to the fact that both the data on deaths and the data on the num
 ber of individuals infected are subject to many different biases. In this 
 presentation\, I consider a Bayesian evidence synthesis approach which\, w
 hile simple enough for researchers to understand and use\, accounts for ma
 ny important sources of bias and uncertainty inherent in both the seroprev
 alence and mortality data. With the understanding that the results of one'
 s evidence synthesis may be largely driven by which studies are included a
 nd which are excluded\, two separate parallel analyses are conducted based
  on two different lists of eligible studies. The various challenges encoun
 tered in estimating the COVID-19 IFR provide valuable lessons for epidemio
 logists conducting evidence synthesis with challenging data.\n\nLearning O
 bjectives\n\nUnderstand the various challenges of working with COVID-19 se
 roprevalence and mortality data and how these challenges can\, to a certai
 n degree\, be addressed with Bayesian methods\n	Discuss how the results of 
 one's evidence synthesis analysis can be greatly impacted by which studies
  are included and which are excluded. It is therefore important to determi
 ne the how the uncertainty inherent in one’s risk of bias assessment can i
 mpact parameter estimates\n	Describe how the lethality of COVID-19 likely v
 aries with population age\, wealth\, and other factors which remain poorly
  understood\, even today\nSpeaker Bio\n\nHarlan Campbell is a statistician
  and is currently working as a postdoctoral research fellow in the Departm
 ent of Statistics at the University of British Columbia. His work focuses 
 on developing statistical methods with a wide range of applications includ
 ing in clinical trials\, epidemiology\, ecology\, and psychology. He is al
 so interested in better understanding the parallels between frequentist an
 d Bayesian paradigms\, and in addressing the so-called reproducibility cri
 sis. He earned his PhD in statistics at the University of British Columbia
 \, after completing his masters at Simon Fraser University\, and his under
 graduate studies at McGill University.\n\nPresented as part of the Epidemi
 ology Seminar Series\n\nThe Department of Epidemiology\, Biostatistics and
  Occupational Health Seminar Series is a self-approved Group Learning Acti
 vity (Section 1) as defined by the maintenance of certification program of
  the Royal College of Physicians and Surgeons of Canada\n
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DTEND:20230220T220000Z
SUMMARY:Determining the lethality of COVID-19: Lessons for addressing bias 
 and uncertainty in evidence synthesis
URL:https://www.mcgill.ca/channels/channels/event/determining-lethality-cov
 id-19-lessons-addressing-bias-and-uncertainty-evidence-synthesis-345577
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