Date: Thursday 15th October
Time: 15:00 - 17:00 BST (16:00 - 18:00 CET)
Speakers: Frank Harrell (Vanderbilt University School of Medicine) and Dean Follmann (Biostatistics Research Branch, NIH)
Who is this event intended for? This webinar is intended for statisticians and data scientists involved in the design and analysis of clinical trials for coronavirus disease treatment or prophylactics.
What 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 selected topics on vaccine induced correlation with immune response consequences of a successful vaccine, on the conduct of ongoing placebo-controlled trials.
You can now register for this event. Registration will close at 12:00 on 14th October 2020.
This event is free of charge to both Members and Non Members of PSI.
To register your place, please click here.
The Vaccine SIG is proud to bring you this webinar, which will feature two presentations on topics relating to methodological developments in vaccines research. We are delighted to be joined by Frank Harrell who will present 'Sequential Bayesian Designs for Rapid Learning 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.
|
Speaker |
Biography |
Abstract |
|
|
Dr. Harrell received his PhD in Biostatistics from the University of North Carolina in 1979. He was on the faculty of Duke University for 17 years and of the University of Virginia for 7 years. He founded the Division of Biostatistics and Epidemiology at the University of Virginia School of Medicine in 1996 and the Department of Biostatistics at Vanderbilt University in 2003. He has taught biostatistics and research methodology to hundreds of physicians since 1980 and has been a mentor or co-mentor to several physician investigators. He is an Associate Editor for Statistics in Medicine, and a member of the Scientific Advisory Board for Science Translational Medicine. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, technology evaluation, quantifying predictive accuracy, missing data imputation, clinical trials, pharmaceutical safety, flexible Bayesian design and analysis, and statistical graphics and reporting. He has worked on a large number of clinical trials.
Dr. Harrell is a Fellow of the American Statistical Association and winner of its 2014 WJ Dixon Award for Excellence in Statistical Consulting. He was the 2008 Mitchell Lecturer for the Department of Statistics, Glasgow University. He was the 2012 Presidential Invited Lecturer for WNAR, International Biometric Society, the 2017 Visionary Speaker, Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics, Chapel Hill, and the 2018 Distinguished Visiting Scientist, University of Calgary 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 Methods program for the Vanderbilt NIH CTSA and was the director of the Statistics 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 Statistics in Medicine and has almost 300 peer-reviewed publications (5 with >1000 citations). |
Sequential Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials 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 simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with 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 treatments. Besides requiring flexibility in a rapidly changing environment, COVID-19 therapeutic trials often use ordinal endpoints and standard statistical models such as the proportional odds (PO) model. Less standard is 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 differently than how it affects other components of the ordinal scale. These ordinal models will be briefly discussed.
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Dr. Follmann is Chief of the Biostatistics Research Branch at the National Institute 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 peer-reviewed research articles and received numerous awards, including the Department of Health and Human Services Secretary’s Award for Distinguished Service, the Best Paper in Biometrics 2009, and is an elected Fellow of the American Statistical Association in 2003. He serves on committees and advisory boards for the US Food and Drug Administration, the National Institutes of Health, and academic departments. Current research interests focus on statistical methods related to vaccinology. |
Statistical Aspects of COVID-19 Vaccine Trials Operation Warp Speed (OWS)is the US government program to evaluate COVID-19 vaccine clinical trials with six different trials launched or planned. The speed, complexity, and scrutiny of the trials in our charged political environment and during a global pandemic is unprecedented. Multiple aspects of the trial require quickly yet carefully 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-depth workup of selected topics including monitoring for vaccine induced enhanced 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.
|
Date: Thursday 15th October
Time: 15:00 - 17:00 BST (16:00 - 18:00 CET)
Speakers: Frank Harrell (Vanderbilt University School of Medicine) and Dean Follmann (Biostatistics Research Branch, NIH)
Who is this event intended for? This webinar is intended for statisticians and data scientists involved in the design and analysis of clinical trials for coronavirus disease treatment or prophylactics.
What 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 selected topics on vaccine induced correlation with immune response consequences of a successful vaccine, on the conduct of ongoing placebo-controlled trials.
You can now register for this event. Registration will close at 12:00 on 14th October 2020.
This event is free of charge to both Members and Non Members of PSI.
To register your place, please click here.
The Vaccine SIG is proud to bring you this webinar, which will feature two presentations on topics relating to methodological developments in vaccines research. We are delighted to be joined by Frank Harrell who will present 'Sequential Bayesian Designs for Rapid Learning 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.
|
Speaker |
Biography |
Abstract |
|
|
Dr. Harrell received his PhD in Biostatistics from the University of North Carolina in 1979. He was on the faculty of Duke University for 17 years and of the University of Virginia for 7 years. He founded the Division of Biostatistics and Epidemiology at the University of Virginia School of Medicine in 1996 and the Department of Biostatistics at Vanderbilt University in 2003. He has taught biostatistics and research methodology to hundreds of physicians since 1980 and has been a mentor or co-mentor to several physician investigators. He is an Associate Editor for Statistics in Medicine, and a member of the Scientific Advisory Board for Science Translational Medicine. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, technology evaluation, quantifying predictive accuracy, missing data imputation, clinical trials, pharmaceutical safety, flexible Bayesian design and analysis, and statistical graphics and reporting. He has worked on a large number of clinical trials.
Dr. Harrell is a Fellow of the American Statistical Association and winner of its 2014 WJ Dixon Award for Excellence in Statistical Consulting. He was the 2008 Mitchell Lecturer for the Department of Statistics, Glasgow University. He was the 2012 Presidential Invited Lecturer for WNAR, International Biometric Society, the 2017 Visionary Speaker, Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics, Chapel Hill, and the 2018 Distinguished Visiting Scientist, University of Calgary 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 Methods program for the Vanderbilt NIH CTSA and was the director of the Statistics 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 Statistics in Medicine and has almost 300 peer-reviewed publications (5 with >1000 citations). |
Sequential Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials 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 simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with 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 treatments. Besides requiring flexibility in a rapidly changing environment, COVID-19 therapeutic trials often use ordinal endpoints and standard statistical models such as the proportional odds (PO) model. Less standard is 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 differently than how it affects other components of the ordinal scale. These ordinal models will be briefly discussed.
|
|
|
Dr. Follmann is Chief of the Biostatistics Research Branch at the National Institute 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 peer-reviewed research articles and received numerous awards, including the Department of Health and Human Services Secretary’s Award for Distinguished Service, the Best Paper in Biometrics 2009, and is an elected Fellow of the American Statistical Association in 2003. He serves on committees and advisory boards for the US Food and Drug Administration, the National Institutes of Health, and academic departments. Current research interests focus on statistical methods related to vaccinology. |
Statistical Aspects of COVID-19 Vaccine Trials Operation Warp Speed (OWS)is the US government program to evaluate COVID-19 vaccine clinical trials with six different trials launched or planned. The speed, complexity, and scrutiny of the trials in our charged political environment and during a global pandemic is unprecedented. Multiple aspects of the trial require quickly yet carefully 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-depth workup of selected topics including monitoring for vaccine induced enhanced 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.
|
Date: Thursday 15th October
Time: 15:00 - 17:00 BST (16:00 - 18:00 CET)
Speakers: Frank Harrell (Vanderbilt University School of Medicine) and Dean Follmann (Biostatistics Research Branch, NIH)
Who is this event intended for? This webinar is intended for statisticians and data scientists involved in the design and analysis of clinical trials for coronavirus disease treatment or prophylactics.
What 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 selected topics on vaccine induced correlation with immune response consequences of a successful vaccine, on the conduct of ongoing placebo-controlled trials.
You can now register for this event. Registration will close at 12:00 on 14th October 2020.
This event is free of charge to both Members and Non Members of PSI.
To register your place, please click here.
The Vaccine SIG is proud to bring you this webinar, which will feature two presentations on topics relating to methodological developments in vaccines research. We are delighted to be joined by Frank Harrell who will present 'Sequential Bayesian Designs for Rapid Learning 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.
|
Speaker |
Biography |
Abstract |
|
|
Dr. Harrell received his PhD in Biostatistics from the University of North Carolina in 1979. He was on the faculty of Duke University for 17 years and of the University of Virginia for 7 years. He founded the Division of Biostatistics and Epidemiology at the University of Virginia School of Medicine in 1996 and the Department of Biostatistics at Vanderbilt University in 2003. He has taught biostatistics and research methodology to hundreds of physicians since 1980 and has been a mentor or co-mentor to several physician investigators. He is an Associate Editor for Statistics in Medicine, and a member of the Scientific Advisory Board for Science Translational Medicine. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, technology evaluation, quantifying predictive accuracy, missing data imputation, clinical trials, pharmaceutical safety, flexible Bayesian design and analysis, and statistical graphics and reporting. He has worked on a large number of clinical trials.
Dr. Harrell is a Fellow of the American Statistical Association and winner of its 2014 WJ Dixon Award for Excellence in Statistical Consulting. He was the 2008 Mitchell Lecturer for the Department of Statistics, Glasgow University. He was the 2012 Presidential Invited Lecturer for WNAR, International Biometric Society, the 2017 Visionary Speaker, Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics, Chapel Hill, and the 2018 Distinguished Visiting Scientist, University of Calgary 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 Methods program for the Vanderbilt NIH CTSA and was the director of the Statistics 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 Statistics in Medicine and has almost 300 peer-reviewed publications (5 with >1000 citations). |
Sequential Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials 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 simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with 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 treatments. Besides requiring flexibility in a rapidly changing environment, COVID-19 therapeutic trials often use ordinal endpoints and standard statistical models such as the proportional odds (PO) model. Less standard is 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 differently than how it affects other components of the ordinal scale. These ordinal models will be briefly discussed.
|
|
|
Dr. Follmann is Chief of the Biostatistics Research Branch at the National Institute 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 peer-reviewed research articles and received numerous awards, including the Department of Health and Human Services Secretary’s Award for Distinguished Service, the Best Paper in Biometrics 2009, and is an elected Fellow of the American Statistical Association in 2003. He serves on committees and advisory boards for the US Food and Drug Administration, the National Institutes of Health, and academic departments. Current research interests focus on statistical methods related to vaccinology. |
Statistical Aspects of COVID-19 Vaccine Trials Operation Warp Speed (OWS)is the US government program to evaluate COVID-19 vaccine clinical trials with six different trials launched or planned. The speed, complexity, and scrutiny of the trials in our charged political environment and during a global pandemic is unprecedented. Multiple aspects of the trial require quickly yet carefully 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-depth workup of selected topics including monitoring for vaccine induced enhanced 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.
|
Date: Thursday 15th October
Time: 15:00 - 17:00 BST (16:00 - 18:00 CET)
Speakers: Frank Harrell (Vanderbilt University School of Medicine) and Dean Follmann (Biostatistics Research Branch, NIH)
Who is this event intended for? This webinar is intended for statisticians and data scientists involved in the design and analysis of clinical trials for coronavirus disease treatment or prophylactics.
What 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 selected topics on vaccine induced correlation with immune response consequences of a successful vaccine, on the conduct of ongoing placebo-controlled trials.
You can now register for this event. Registration will close at 12:00 on 14th October 2020.
This event is free of charge to both Members and Non Members of PSI.
To register your place, please click here.
The Vaccine SIG is proud to bring you this webinar, which will feature two presentations on topics relating to methodological developments in vaccines research. We are delighted to be joined by Frank Harrell who will present 'Sequential Bayesian Designs for Rapid Learning 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.
|
Speaker |
Biography |
Abstract |
|
|
Dr. Harrell received his PhD in Biostatistics from the University of North Carolina in 1979. He was on the faculty of Duke University for 17 years and of the University of Virginia for 7 years. He founded the Division of Biostatistics and Epidemiology at the University of Virginia School of Medicine in 1996 and the Department of Biostatistics at Vanderbilt University in 2003. He has taught biostatistics and research methodology to hundreds of physicians since 1980 and has been a mentor or co-mentor to several physician investigators. He is an Associate Editor for Statistics in Medicine, and a member of the Scientific Advisory Board for Science Translational Medicine. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, technology evaluation, quantifying predictive accuracy, missing data imputation, clinical trials, pharmaceutical safety, flexible Bayesian design and analysis, and statistical graphics and reporting. He has worked on a large number of clinical trials.
Dr. Harrell is a Fellow of the American Statistical Association and winner of its 2014 WJ Dixon Award for Excellence in Statistical Consulting. He was the 2008 Mitchell Lecturer for the Department of Statistics, Glasgow University. He was the 2012 Presidential Invited Lecturer for WNAR, International Biometric Society, the 2017 Visionary Speaker, Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics, Chapel Hill, and the 2018 Distinguished Visiting Scientist, University of Calgary 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 Methods program for the Vanderbilt NIH CTSA and was the director of the Statistics 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 Statistics in Medicine and has almost 300 peer-reviewed publications (5 with >1000 citations). |
Sequential Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials 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 simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with 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 treatments. Besides requiring flexibility in a rapidly changing environment, COVID-19 therapeutic trials often use ordinal endpoints and standard statistical models such as the proportional odds (PO) model. Less standard is 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 differently than how it affects other components of the ordinal scale. These ordinal models will be briefly discussed.
|
|
|
Dr. Follmann is Chief of the Biostatistics Research Branch at the National Institute 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 peer-reviewed research articles and received numerous awards, including the Department of Health and Human Services Secretary’s Award for Distinguished Service, the Best Paper in Biometrics 2009, and is an elected Fellow of the American Statistical Association in 2003. He serves on committees and advisory boards for the US Food and Drug Administration, the National Institutes of Health, and academic departments. Current research interests focus on statistical methods related to vaccinology. |
Statistical Aspects of COVID-19 Vaccine Trials Operation Warp Speed (OWS)is the US government program to evaluate COVID-19 vaccine clinical trials with six different trials launched or planned. The speed, complexity, and scrutiny of the trials in our charged political environment and during a global pandemic is unprecedented. Multiple aspects of the trial require quickly yet carefully 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-depth workup of selected topics including monitoring for vaccine induced enhanced 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.
|
Date: Thursday 15th October
Time: 15:00 - 17:00 BST (16:00 - 18:00 CET)
Speakers: Frank Harrell (Vanderbilt University School of Medicine) and Dean Follmann (Biostatistics Research Branch, NIH)
Who is this event intended for? This webinar is intended for statisticians and data scientists involved in the design and analysis of clinical trials for coronavirus disease treatment or prophylactics.
What 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 selected topics on vaccine induced correlation with immune response consequences of a successful vaccine, on the conduct of ongoing placebo-controlled trials.
You can now register for this event. Registration will close at 12:00 on 14th October 2020.
This event is free of charge to both Members and Non Members of PSI.
To register your place, please click here.
The Vaccine SIG is proud to bring you this webinar, which will feature two presentations on topics relating to methodological developments in vaccines research. We are delighted to be joined by Frank Harrell who will present 'Sequential Bayesian Designs for Rapid Learning 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.
|
Speaker |
Biography |
Abstract |
|
|
Dr. Harrell received his PhD in Biostatistics from the University of North Carolina in 1979. He was on the faculty of Duke University for 17 years and of the University of Virginia for 7 years. He founded the Division of Biostatistics and Epidemiology at the University of Virginia School of Medicine in 1996 and the Department of Biostatistics at Vanderbilt University in 2003. He has taught biostatistics and research methodology to hundreds of physicians since 1980 and has been a mentor or co-mentor to several physician investigators. He is an Associate Editor for Statistics in Medicine, and a member of the Scientific Advisory Board for Science Translational Medicine. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, technology evaluation, quantifying predictive accuracy, missing data imputation, clinical trials, pharmaceutical safety, flexible Bayesian design and analysis, and statistical graphics and reporting. He has worked on a large number of clinical trials.
Dr. Harrell is a Fellow of the American Statistical Association and winner of its 2014 WJ Dixon Award for Excellence in Statistical Consulting. He was the 2008 Mitchell Lecturer for the Department of Statistics, Glasgow University. He was the 2012 Presidential Invited Lecturer for WNAR, International Biometric Society, the 2017 Visionary Speaker, Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics, Chapel Hill, and the 2018 Distinguished Visiting Scientist, University of Calgary 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 Methods program for the Vanderbilt NIH CTSA and was the director of the Statistics 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 Statistics in Medicine and has almost 300 peer-reviewed publications (5 with >1000 citations). |
Sequential Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials 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 simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with 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 treatments. Besides requiring flexibility in a rapidly changing environment, COVID-19 therapeutic trials often use ordinal endpoints and standard statistical models such as the proportional odds (PO) model. Less standard is 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 differently than how it affects other components of the ordinal scale. These ordinal models will be briefly discussed.
|
|
|
Dr. Follmann is Chief of the Biostatistics Research Branch at the National Institute 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 peer-reviewed research articles and received numerous awards, including the Department of Health and Human Services Secretary’s Award for Distinguished Service, the Best Paper in Biometrics 2009, and is an elected Fellow of the American Statistical Association in 2003. He serves on committees and advisory boards for the US Food and Drug Administration, the National Institutes of Health, and academic departments. Current research interests focus on statistical methods related to vaccinology. |
Statistical Aspects of COVID-19 Vaccine Trials Operation Warp Speed (OWS)is the US government program to evaluate COVID-19 vaccine clinical trials with six different trials launched or planned. The speed, complexity, and scrutiny of the trials in our charged political environment and during a global pandemic is unprecedented. Multiple aspects of the trial require quickly yet carefully 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-depth workup of selected topics including monitoring for vaccine induced enhanced 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.
|
Date: Thursday 15th October
Time: 15:00 - 17:00 BST (16:00 - 18:00 CET)
Speakers: Frank Harrell (Vanderbilt University School of Medicine) and Dean Follmann (Biostatistics Research Branch, NIH)
Who is this event intended for? This webinar is intended for statisticians and data scientists involved in the design and analysis of clinical trials for coronavirus disease treatment or prophylactics.
What 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 selected topics on vaccine induced correlation with immune response consequences of a successful vaccine, on the conduct of ongoing placebo-controlled trials.
You can now register for this event. Registration will close at 12:00 on 14th October 2020.
This event is free of charge to both Members and Non Members of PSI.
To register your place, please click here.
The Vaccine SIG is proud to bring you this webinar, which will feature two presentations on topics relating to methodological developments in vaccines research. We are delighted to be joined by Frank Harrell who will present 'Sequential Bayesian Designs for Rapid Learning 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.
|
Speaker |
Biography |
Abstract |
|
|
Dr. Harrell received his PhD in Biostatistics from the University of North Carolina in 1979. He was on the faculty of Duke University for 17 years and of the University of Virginia for 7 years. He founded the Division of Biostatistics and Epidemiology at the University of Virginia School of Medicine in 1996 and the Department of Biostatistics at Vanderbilt University in 2003. He has taught biostatistics and research methodology to hundreds of physicians since 1980 and has been a mentor or co-mentor to several physician investigators. He is an Associate Editor for Statistics in Medicine, and a member of the Scientific Advisory Board for Science Translational Medicine. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, technology evaluation, quantifying predictive accuracy, missing data imputation, clinical trials, pharmaceutical safety, flexible Bayesian design and analysis, and statistical graphics and reporting. He has worked on a large number of clinical trials.
Dr. Harrell is a Fellow of the American Statistical Association and winner of its 2014 WJ Dixon Award for Excellence in Statistical Consulting. He was the 2008 Mitchell Lecturer for the Department of Statistics, Glasgow University. He was the 2012 Presidential Invited Lecturer for WNAR, International Biometric Society, the 2017 Visionary Speaker, Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics, Chapel Hill, and the 2018 Distinguished Visiting Scientist, University of Calgary 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 Methods program for the Vanderbilt NIH CTSA and was the director of the Statistics 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 Statistics in Medicine and has almost 300 peer-reviewed publications (5 with >1000 citations). |
Sequential Bayesian Designs for Rapid Learning in COVID-19 Therapeutic Trials 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 simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with 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 treatments. Besides requiring flexibility in a rapidly changing environment, COVID-19 therapeutic trials often use ordinal endpoints and standard statistical models such as the proportional odds (PO) model. Less standard is 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 differently than how it affects other components of the ordinal scale. These ordinal models will be briefly discussed.
|
|
|
Dr. Follmann is Chief of the Biostatistics Research Branch at the National Institute 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 peer-reviewed research articles and received numerous awards, including the Department of Health and Human Services Secretary’s Award for Distinguished Service, the Best Paper in Biometrics 2009, and is an elected Fellow of the American Statistical Association in 2003. He serves on committees and advisory boards for the US Food and Drug Administration, the National Institutes of Health, and academic departments. Current research interests focus on statistical methods related to vaccinology. |
Statistical Aspects of COVID-19 Vaccine Trials Operation Warp Speed (OWS)is the US government program to evaluate COVID-19 vaccine clinical trials with six different trials launched or planned. The speed, complexity, and scrutiny of the trials in our charged political environment and during a global pandemic is unprecedented. Multiple aspects of the trial require quickly yet carefully 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-depth workup of selected topics including monitoring for vaccine induced enhanced 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.
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