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DTSTART:20231002T020000
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BEGIN:VEVENT
DESCRIPTION:Date: Thursday 15th October\nTime: 15:00 - 17:00 BST (16:00 - 1
8:00 CET)\nSpeakers: \;Frank Harrell (Vanderbilt University School of
Medicine) \;and \;Dean Follmann (Biostatistics Research Branch\, N
IH)\n\nWho is this event intended for? This webinar is intended for statis
ticians and data scientists involved in the design and analysis of clinica
l trials for coronavirus disease treatment or prophylactics.\nWhat is the
benefit of attending? Attendees will have the opportunity to hear from two
key experts in the field about novel Bayesian techniques\, as well as sel
ected topics on vaccine induced correlation with immune response consequen
ces of a successful vaccine\, on the conduct of ongoing placebo-controlled
trials.\n\n\nRegistration\nYou can now register for this event. Registrat
ion will close at 12:00 on 14th October 2020.\nThis event is free of charg
e to both Members and Non Members of PSI.\nTo register your place\, please
click here.\nOverview\nThe Vaccine SIG is proud to bring you this webinar
\, which will feature two presentations on \;topics relating to method
ological developments in vaccines research. We are delighted to be joined
by \;Frank Harrell who will present 'Sequential Bayesian Designs for R
apid Learning in COVID-19 Therapeutic Trials'\; and also by \;Dean Fol
lmann\, who will present on 'Statistical Aspects of COVID-19 Vaccine Trial
s'. Join us for this insightful and highly topical webinar.\nSpeaker Detai
ls\n\n\n\n \n \n \n Speaker\n \
n  \;Biography\n \n Abstract\n
\n \n \n \n \n \n
\n \n \n \n \n
\n \n \n \n \n \
n Frank Harrell\n Department of Biostatistics\, Vand
erbilt University School of Medicine\n \n Dr. \;
Harrell received his PhD in Biostatistics from the University of North Car
olina in 1979. He was on the faculty of Duke University for 17 years and o
f the University of Virginia for 7 years. He founded the Division of Biost
atistics and Epidemiology at the University of Virginia School of Medicine
in 1996 and the Department of Biostatistics at Vanderbilt University in 2
003. He has taught biostatistics and research methodology to hundreds of p
hysicians since 1980 and has been a mentor or co-mentor to several physici
an investigators. He is an Associate Editor for Statistics in Medicine\, a
nd a member of the Scientific Advisory Board for Science Translational Med
icine. His specialties are development of accurate prognostic and diagnost
ic models\, model validation\, clinical trials\, observational clinical re
search\, technology evaluation\, quantifying predictive accuracy\, missing
data imputation\, clinical trials\, pharmaceutical safety\, flexible Baye
sian design and analysis\, and statistical graphics and reporting. He has
worked on a large number of clinical trials.\n Dr. \;Harrel
l is a Fellow of the American Statistical Association and winner of its 20
14 WJ Dixon Award for Excellence in Statistical Consulting. He was the 200
8 Mitchell Lecturer for the Department of Statistics\, Glasgow University.
He was the 2012 Presidential Invited Lecturer for WNAR\, International Bi
ometric Society\, the 2017 Visionary Speaker\, Clinical Studies Coordinati
ng Center\, University of North Carolina Department of Biostatistics\, Cha
pel Hill\, and the 2018 Distinguished Visiting Scientist\, University of C
algary Biostatistics Centre. He was an FDA Expert Statistical Advisor from
2016-2020 and was a member of the NIH Biostatistical Methods and Research
Design Study Section. He is the associate director of the Research Method
s program for the Vanderbilt NIH CTSA and was the director of the Statisti
cs and Methodology Core for the Vanderbilt Kennedy Center for Research on
Human Development. He is the PI of the NHLBI multinational ISCHEMIA trial
DSMB statistical center. He is the author of two of the most highly cited
papers (both are on development of prognostic models) in the history of St
atistics in Medicine and has almost 300 peer-reviewed publications (5 with
>\;1000 citations).\n \n \n Sequential
Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials\n
Continuous learning from data and computation of probabilities that
are directly applicable to decision making in the face of uncertainty are
hallmarks of the Bayesian approach. Bayesian sequential designs are the s
implest of flexible designs\, and continuous learning capitalizes on their
efficiency\, resulting in lower expected sample sizes until sufficient ev
idence is accrued due to the ability to take unlimited data looks. Classic
al null hypothesis testing only provides evidence against the supposition
that a treatment has exactly zero effect\, and it requires one to deal wit
h complexities if not doing the analysis at a single fixed time. Bayesian
posterior probabilities\, on the other hand\, can be computed at any point
in the trial and provide current evidence about all possible questions\,
such as benefit\, clinically relevant benefit\, harm\, and similarity of t
reatments.\n Besides requiring flexibility in a rapidly changin
g environment\, COVID-19 therapeutic trials often use ordinal endpoints an
d standard statistical models such as the proportional odds (PO) model. Le
ss standard is how to model serial ordinal responses. Methods and new Baye
sian software have been developed for COVID-19 therapeutic trials. Also im
plemented is a Bayesian partial PO model (Peterson and Harrell\, 1990) tha
t allows one to put a prior on the degree to which a treatment affects mor
tality differently than how it affects other components of the ordinal sca
le. These ordinal models will be briefly discussed.\n  \;\n
\n \n \n \n \n
\n \n \n \n \n \n
\n \n \n \n \n
\n Dean Follmann\n Chief\, Biostatistics Resea
rch Branch\, NIH\n \n Dr. Follmann is Chief of the B
iostatistics Research Branch at the National Institute of Allergy and Infe
ctious Diseases (NIAID)\, a role he has held for the past 16 years. \;
He has authored or co-authored more than 250 peer-reviewed research artic
les and received numerous awards\, including the Department of Health and
Human Services Secretary&rsquo\;s Award for Distinguished Service\, the Be
st Paper in Biometrics 2009\, and is an elected Fellow of the American Sta
tistical Association in 2003. \; He serves on committees and advisory
boards for the US Food and Drug Administration\, the National Institutes o
f Health\, and academic departments. \;Current research interests focu
s on statistical methods related to vaccinology.\n \n
Statistical Aspects of COVID-19 Vaccine Trials\n Operation Wa
rp Speed (OWS)is the US government program to evaluate COVID-19 vaccine cl
inical trials with six different trials launched or planned. The speed\, c
omplexity\, and scrutiny of the trials\n in our charged politic
al environment and during a global pandemic is unprecedented. Multiple asp
ects of the trial require quickly yet carefully crafted statistical approa
ches for design\, monitoring\, and analysis.In this talk we give a brief o
verview of the OWS landscape\, discuss the basic structure ofvaccine clini
cal trials\, and then provide a more in-depth workup of selected topics in
cluding monitoring for vaccine induced enhanced disease\,correlating vacci
ne induced immune response to prevention of disease\, and the consequences
of a successful vaccine on the conduct of ongoing placebo-controlled tria
ls.\n  \;\n  \;\n \n \n
\n\n \;
DTEND:20201015T160000Z
DTSTAMP:20240329T155027Z
DTSTART:20201015T140000Z
LOCATION:
SEQUENCE:0
SUMMARY:PSI Vaccine SIG Webinar: Statistical Topics on COVID-19 Therapeutic
and Vaccine Clinical Trials
UID:RFCALITEM638473242276574289
X-ALT-DESC;FMTTYPE=text/html:Date: Thursday 15th October\nTime: 15:00 - 17:00 BST (16:00 - 18:00 CET)
\n
Speakers: \;Frank Harrell (Vanderbilt University
School of Medicine) \;and \;Dean Follmann (Biostatistics
Research Branch\, NIH)
\n
\nWho is this event inten
ded for? This webinar is intended for statisticians and data scie
ntists involved in the design and analysis of clinical trials for coronavi
rus disease treatment or prophylactics.
\nWhat is the benefit
of attending? Attendees will have the opportunity to hear from t
wo key experts in the field about novel Bayesian techniques\, as well as s
elected topics on vaccine induced correlation with immune response consequ
ences of a successful vaccine\, on the conduct of ongoing placebo-controll
ed trials.
\n
\n\n
You can now
register for this event. Registration will close at 12:00 on 14th October
2020.
\nThis event is free of charge to both Member
s and Non Members of PSI.
\nTo register your place\, please <
a href="https://members.psiweb.org/Core_Content_PSI/Events/Event_Display.a
spx?EventKey=226" target="_blank">click here.
The Vaccine SIG is proud to bring you this webinar\, which wil l feature two presentations on \;topics relating to methodological dev elopments in vaccines research. We are delighted to be joined by \;Fra nk Harrell who will present 'Sequential Bayesian Designs for Rapid Learnin g in COVID-19 Therapeutic Trials'\; and also by \;Dean Follmann\, who will present on 'Statistical Aspects of COVID-19 Vaccine Trials'. Join us for this insightful and highly topical webinar.
\n\n Speaker \n | \n  \;Biography | \n|
\n Sequential Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials \nContinuous le arning from data and computation of probabilities that are directly applic able to decision making in the face of uncertainty are hallmarks of the Ba yesian approach. Bayesian sequential designs are the simplest of flexible designs\, and continuous learning capitalizes on their efficiency\, result ing in lower expected sample sizes until sufficient evidence is accrued du e to the ability to take unlimited data looks. Classical null hypothesis t esting only provides evidence against the supposition that a treatment has exactly zero effect\, and it requires one to deal with complexities if no t doing the analysis at a single fixed time. Bayesian posterior probabilit ies\, on the other hand\, can be computed at any point in the trial and pr ovide current evidence about all possible questions\, such as benefit\, cl inically relevant benefit\, harm\, and similarity of treatments. \nBesides requiring flexibility in a rapidly changing environment \, COVID-19 therapeutic trials often use ordinal endpoints and standard st atistical models such as the proportional odds (PO) model. Less standard i s how to model serial ordinal responses. Methods and new Bayesian software have been developed for COVID-19 therapeutic trials. Also implemented is a Bayesian partial PO model (Peterson and Harrell\, 1990) that allows one to put a prior on the degree to which a treatment affects mortality differ ently than how it affects other components of the ordinal scale. These ord inal models will be briefly discussed. \n \; \n | \n ||
\n
| \n Dr. Follmann is Chief of the Biostatistics Research Branch at the National Ins titute of Allergy and Infectious Diseases (NIAID)\, a role he has held for the past 16 years. \; He has authored or co-authored more than 250 pe er-reviewed research articles and received numerous awards\, including the Department of Health and Human Services Secretary&rsquo\;s Award for Dist inguished Service\, the Best Paper in Biometrics 2009\, and is an elected Fellow of the American Statistical Association in 2003. \; He serves o n committees and advisory boards for the US Food and Drug Administration\, the National Institutes of Health\, and academic departments. \;Curre nt research interests focus on statistical methods related to vaccinology. | \n\n <
p>Statistical Aspects of COVID-19 Vaccine Trials\n
Operation Warp Speed (OWS)is the US government program to eval uate COVID-19 vaccine clinical trials with six different trials launched o r planned. The speed\, complexity\, and scrutiny of the trials \nin our charged political environment and during a global pandemic is unprecedented. Multiple aspects of the trial require quickly yet caref ully crafted statistical approaches for design\, monitoring\, and analysis .In this talk we give a brief overview of the OWS landscape\, discuss the basic structure ofvaccine clinical trials\, and then provide a more in-dep th workup of selected topics including monitoring for vaccine induced enha nced disease\,correlating vaccine induced immune response to prevention of disease\, and the consequences of a successful vaccine on the conduct of ongoing placebo-controlled trials. \n \; \n \; \n | \n