Date: Monday 24th - Thursday 27th January 2022 Time: Lectures 09:00-12:00 on the 24th & 27th. Separate forum setup for practical exercises between the 24th-26th. Speakers: Sebastian Weber and Gaëlle Saint-Hilary
Who is this event intended for? All statisticians working on clinical trials, with a basic knowledge of R. Some notions of Bayesian statistics could be helpful. What is the benefit of attending? Attendees will have the chance to cover; Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model; Design planning, operating characteristics, statistical analysis; and Applications using the R package RBesT.
Course Cost
Regular Members = £340+VAT Regular Non-Members = £465*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2022.
There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. Including external data in the analysis of clinical trials may increase study power or allow for the reduction of (usually) the control group sample size. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012; Smith et al., 2019), occasionally in phase III trials (French et al., 2012; Viele et al., 2018), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989; Best et al., 2019). This allows adequately powered trials at smaller sample sizes leading to faster trial conduct and exposure of fewer patients to a potentially in-effective control treatment.
In this short course, we will provide a statistical framework to incorporate external information into a trial. During the first part of the course, we will introduce Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model (Neuenschwander et al., 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources. Dynamic borrowing permits to limit the use of historical data when it is incompatible with the data observed within the trial.
In the second part of the course, we will propose a practice session with applications using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. These exercises will enable participants to apply the presented approach themselves. During third and last part of the course, more advanced topics will be detailed such as effective and maximum sample sizes, advanced operating characteristics, and probability of success.
Agenda
Lecture session 1 (morning of Monday 24th January (09:00-12:00))
• Meta-analytic predictive (MAP) model
• Robustification for dynamic borrowing
• Design planning, operating characteristics
• Final analysis Practice sessions (afternoon of Monday 24th, all day Tuesday 25th, all day Wednesday 26th)
• Supervised homework, set-up in a separate forum Lecture session 2 (morning of Thursday 27th January (09:00-12:00))
• Effective sample size, maximum sample size
• Advanced operating characteristics
• Equivalence between MAP and MAC (Meta-analysis combined)
• Probability of success, decision rule
Speaker details
Speaker
Biography
Gaëlle Saint-Hilary
Gaëlle Saint-Hilary is Statistical Methodologist, CEO and founder of the consulting company Saryga (France). Prior to this role, Gaëlle was Statistical Methodologist at Servier until December 2021. With more than 15 years of experience in the pharmaceutical industry (Servier, Novartis) and a strong and long-lasting collaboration with academia, Gaëlle Saint-Hilary is an expert in Bayesian statistics and decision-making support. She has worked at developing novel approaches to improve drug development’s performances, and her main scientific interests are quantitative decision-making, benefit-risk assessment, innovative study designs and historical data.
Sebastian Weber
Sebastian Weber is working as Director in the Department of Advanced Methodology and Data Science at Novartis. He holds a PhD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He has worked extensively on enabling the use of historical (control) information in clinical trials through consulting and working on tools to facilitate the application of historical control information from trial design to analysis. Furthermore, Sebastian has experience in designing Oncology phase I dose-escalation trails and is also involved in pediatric drug development programs, where he applies extrapolation concepts. His research interests include the application of pharmacometrics in statistics, model-based drug development and application of Bayesian methods for drug development.
Disclaimer
PSI is a non-profit organisation run by volunteers. Many of the event organisers and presenters donate their time, while the majority of the event registration cost is spent on administrative support, venue rental / online conferencing, travel costs for the presenter, software licences, and general running of the society. PSI strives to offer high quality courses, but cannot offer a guarantee that the content presented is accurate or fit for your particular needs. Please check if any software is required for this course and ensure you are able to run it prior to registering.
Cancellation and Moderation Terms For cancellations received up to two weeks prior to a PSI event start-date, the event registration fee will be refunded less 25% administrative charge. After this date, no refunds will be possible. A handling fee of 20 GBP per registration will be charged for every registration modification received two weeks prior or less, including a delegate name change.
Scientific Meetings
PSI Training Course: Use of Historical Data in Clinical Trials: An Evidence Synthesis Approach
Date: Monday 24th - Thursday 27th January 2022 Time: Lectures 09:00-12:00 on the 24th & 27th. Separate forum setup for practical exercises between the 24th-26th. Speakers: Sebastian Weber and Gaëlle Saint-Hilary
Who is this event intended for? All statisticians working on clinical trials, with a basic knowledge of R. Some notions of Bayesian statistics could be helpful. What is the benefit of attending? Attendees will have the chance to cover; Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model; Design planning, operating characteristics, statistical analysis; and Applications using the R package RBesT.
Course Cost
Regular Members = £340+VAT Regular Non-Members = £465*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2022.
There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. Including external data in the analysis of clinical trials may increase study power or allow for the reduction of (usually) the control group sample size. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012; Smith et al., 2019), occasionally in phase III trials (French et al., 2012; Viele et al., 2018), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989; Best et al., 2019). This allows adequately powered trials at smaller sample sizes leading to faster trial conduct and exposure of fewer patients to a potentially in-effective control treatment.
In this short course, we will provide a statistical framework to incorporate external information into a trial. During the first part of the course, we will introduce Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model (Neuenschwander et al., 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources. Dynamic borrowing permits to limit the use of historical data when it is incompatible with the data observed within the trial.
In the second part of the course, we will propose a practice session with applications using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. These exercises will enable participants to apply the presented approach themselves. During third and last part of the course, more advanced topics will be detailed such as effective and maximum sample sizes, advanced operating characteristics, and probability of success.
Agenda
Lecture session 1 (morning of Monday 24th January (09:00-12:00))
• Meta-analytic predictive (MAP) model
• Robustification for dynamic borrowing
• Design planning, operating characteristics
• Final analysis Practice sessions (afternoon of Monday 24th, all day Tuesday 25th, all day Wednesday 26th)
• Supervised homework, set-up in a separate forum Lecture session 2 (morning of Thursday 27th January (09:00-12:00))
• Effective sample size, maximum sample size
• Advanced operating characteristics
• Equivalence between MAP and MAC (Meta-analysis combined)
• Probability of success, decision rule
Speaker details
Speaker
Biography
Gaëlle Saint-Hilary
Gaëlle Saint-Hilary is Statistical Methodologist, CEO and founder of the consulting company Saryga (France). Prior to this role, Gaëlle was Statistical Methodologist at Servier until December 2021. With more than 15 years of experience in the pharmaceutical industry (Servier, Novartis) and a strong and long-lasting collaboration with academia, Gaëlle Saint-Hilary is an expert in Bayesian statistics and decision-making support. She has worked at developing novel approaches to improve drug development’s performances, and her main scientific interests are quantitative decision-making, benefit-risk assessment, innovative study designs and historical data.
Sebastian Weber
Sebastian Weber is working as Director in the Department of Advanced Methodology and Data Science at Novartis. He holds a PhD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He has worked extensively on enabling the use of historical (control) information in clinical trials through consulting and working on tools to facilitate the application of historical control information from trial design to analysis. Furthermore, Sebastian has experience in designing Oncology phase I dose-escalation trails and is also involved in pediatric drug development programs, where he applies extrapolation concepts. His research interests include the application of pharmacometrics in statistics, model-based drug development and application of Bayesian methods for drug development.
Disclaimer
PSI is a non-profit organisation run by volunteers. Many of the event organisers and presenters donate their time, while the majority of the event registration cost is spent on administrative support, venue rental / online conferencing, travel costs for the presenter, software licences, and general running of the society. PSI strives to offer high quality courses, but cannot offer a guarantee that the content presented is accurate or fit for your particular needs. Please check if any software is required for this course and ensure you are able to run it prior to registering.
Cancellation and Moderation Terms For cancellations received up to two weeks prior to a PSI event start-date, the event registration fee will be refunded less 25% administrative charge. After this date, no refunds will be possible. A handling fee of 20 GBP per registration will be charged for every registration modification received two weeks prior or less, including a delegate name change.
Training Courses
PSI Training Course: Use of Historical Data in Clinical Trials: An Evidence Synthesis Approach
Date: Monday 24th - Thursday 27th January 2022 Time: Lectures 09:00-12:00 on the 24th & 27th. Separate forum setup for practical exercises between the 24th-26th. Speakers: Sebastian Weber and Gaëlle Saint-Hilary
Who is this event intended for? All statisticians working on clinical trials, with a basic knowledge of R. Some notions of Bayesian statistics could be helpful. What is the benefit of attending? Attendees will have the chance to cover; Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model; Design planning, operating characteristics, statistical analysis; and Applications using the R package RBesT.
Course Cost
Regular Members = £340+VAT Regular Non-Members = £465*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2022.
There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. Including external data in the analysis of clinical trials may increase study power or allow for the reduction of (usually) the control group sample size. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012; Smith et al., 2019), occasionally in phase III trials (French et al., 2012; Viele et al., 2018), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989; Best et al., 2019). This allows adequately powered trials at smaller sample sizes leading to faster trial conduct and exposure of fewer patients to a potentially in-effective control treatment.
In this short course, we will provide a statistical framework to incorporate external information into a trial. During the first part of the course, we will introduce Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model (Neuenschwander et al., 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources. Dynamic borrowing permits to limit the use of historical data when it is incompatible with the data observed within the trial.
In the second part of the course, we will propose a practice session with applications using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. These exercises will enable participants to apply the presented approach themselves. During third and last part of the course, more advanced topics will be detailed such as effective and maximum sample sizes, advanced operating characteristics, and probability of success.
Agenda
Lecture session 1 (morning of Monday 24th January (09:00-12:00))
• Meta-analytic predictive (MAP) model
• Robustification for dynamic borrowing
• Design planning, operating characteristics
• Final analysis Practice sessions (afternoon of Monday 24th, all day Tuesday 25th, all day Wednesday 26th)
• Supervised homework, set-up in a separate forum Lecture session 2 (morning of Thursday 27th January (09:00-12:00))
• Effective sample size, maximum sample size
• Advanced operating characteristics
• Equivalence between MAP and MAC (Meta-analysis combined)
• Probability of success, decision rule
Speaker details
Speaker
Biography
Gaëlle Saint-Hilary
Gaëlle Saint-Hilary is Statistical Methodologist, CEO and founder of the consulting company Saryga (France). Prior to this role, Gaëlle was Statistical Methodologist at Servier until December 2021. With more than 15 years of experience in the pharmaceutical industry (Servier, Novartis) and a strong and long-lasting collaboration with academia, Gaëlle Saint-Hilary is an expert in Bayesian statistics and decision-making support. She has worked at developing novel approaches to improve drug development’s performances, and her main scientific interests are quantitative decision-making, benefit-risk assessment, innovative study designs and historical data.
Sebastian Weber
Sebastian Weber is working as Director in the Department of Advanced Methodology and Data Science at Novartis. He holds a PhD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He has worked extensively on enabling the use of historical (control) information in clinical trials through consulting and working on tools to facilitate the application of historical control information from trial design to analysis. Furthermore, Sebastian has experience in designing Oncology phase I dose-escalation trails and is also involved in pediatric drug development programs, where he applies extrapolation concepts. His research interests include the application of pharmacometrics in statistics, model-based drug development and application of Bayesian methods for drug development.
Disclaimer
PSI is a non-profit organisation run by volunteers. Many of the event organisers and presenters donate their time, while the majority of the event registration cost is spent on administrative support, venue rental / online conferencing, travel costs for the presenter, software licences, and general running of the society. PSI strives to offer high quality courses, but cannot offer a guarantee that the content presented is accurate or fit for your particular needs. Please check if any software is required for this course and ensure you are able to run it prior to registering.
Cancellation and Moderation Terms For cancellations received up to two weeks prior to a PSI event start-date, the event registration fee will be refunded less 25% administrative charge. After this date, no refunds will be possible. A handling fee of 20 GBP per registration will be charged for every registration modification received two weeks prior or less, including a delegate name change.
Journal Club
PSI Training Course: Use of Historical Data in Clinical Trials: An Evidence Synthesis Approach
Date: Monday 24th - Thursday 27th January 2022 Time: Lectures 09:00-12:00 on the 24th & 27th. Separate forum setup for practical exercises between the 24th-26th. Speakers: Sebastian Weber and Gaëlle Saint-Hilary
Who is this event intended for? All statisticians working on clinical trials, with a basic knowledge of R. Some notions of Bayesian statistics could be helpful. What is the benefit of attending? Attendees will have the chance to cover; Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model; Design planning, operating characteristics, statistical analysis; and Applications using the R package RBesT.
Course Cost
Regular Members = £340+VAT Regular Non-Members = £465*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2022.
There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. Including external data in the analysis of clinical trials may increase study power or allow for the reduction of (usually) the control group sample size. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012; Smith et al., 2019), occasionally in phase III trials (French et al., 2012; Viele et al., 2018), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989; Best et al., 2019). This allows adequately powered trials at smaller sample sizes leading to faster trial conduct and exposure of fewer patients to a potentially in-effective control treatment.
In this short course, we will provide a statistical framework to incorporate external information into a trial. During the first part of the course, we will introduce Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model (Neuenschwander et al., 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources. Dynamic borrowing permits to limit the use of historical data when it is incompatible with the data observed within the trial.
In the second part of the course, we will propose a practice session with applications using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. These exercises will enable participants to apply the presented approach themselves. During third and last part of the course, more advanced topics will be detailed such as effective and maximum sample sizes, advanced operating characteristics, and probability of success.
Agenda
Lecture session 1 (morning of Monday 24th January (09:00-12:00))
• Meta-analytic predictive (MAP) model
• Robustification for dynamic borrowing
• Design planning, operating characteristics
• Final analysis Practice sessions (afternoon of Monday 24th, all day Tuesday 25th, all day Wednesday 26th)
• Supervised homework, set-up in a separate forum Lecture session 2 (morning of Thursday 27th January (09:00-12:00))
• Effective sample size, maximum sample size
• Advanced operating characteristics
• Equivalence between MAP and MAC (Meta-analysis combined)
• Probability of success, decision rule
Speaker details
Speaker
Biography
Gaëlle Saint-Hilary
Gaëlle Saint-Hilary is Statistical Methodologist, CEO and founder of the consulting company Saryga (France). Prior to this role, Gaëlle was Statistical Methodologist at Servier until December 2021. With more than 15 years of experience in the pharmaceutical industry (Servier, Novartis) and a strong and long-lasting collaboration with academia, Gaëlle Saint-Hilary is an expert in Bayesian statistics and decision-making support. She has worked at developing novel approaches to improve drug development’s performances, and her main scientific interests are quantitative decision-making, benefit-risk assessment, innovative study designs and historical data.
Sebastian Weber
Sebastian Weber is working as Director in the Department of Advanced Methodology and Data Science at Novartis. He holds a PhD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He has worked extensively on enabling the use of historical (control) information in clinical trials through consulting and working on tools to facilitate the application of historical control information from trial design to analysis. Furthermore, Sebastian has experience in designing Oncology phase I dose-escalation trails and is also involved in pediatric drug development programs, where he applies extrapolation concepts. His research interests include the application of pharmacometrics in statistics, model-based drug development and application of Bayesian methods for drug development.
Disclaimer
PSI is a non-profit organisation run by volunteers. Many of the event organisers and presenters donate their time, while the majority of the event registration cost is spent on administrative support, venue rental / online conferencing, travel costs for the presenter, software licences, and general running of the society. PSI strives to offer high quality courses, but cannot offer a guarantee that the content presented is accurate or fit for your particular needs. Please check if any software is required for this course and ensure you are able to run it prior to registering.
Cancellation and Moderation Terms For cancellations received up to two weeks prior to a PSI event start-date, the event registration fee will be refunded less 25% administrative charge. After this date, no refunds will be possible. A handling fee of 20 GBP per registration will be charged for every registration modification received two weeks prior or less, including a delegate name change.
Webinars
PSI Training Course: Use of Historical Data in Clinical Trials: An Evidence Synthesis Approach
Date: Monday 24th - Thursday 27th January 2022 Time: Lectures 09:00-12:00 on the 24th & 27th. Separate forum setup for practical exercises between the 24th-26th. Speakers: Sebastian Weber and Gaëlle Saint-Hilary
Who is this event intended for? All statisticians working on clinical trials, with a basic knowledge of R. Some notions of Bayesian statistics could be helpful. What is the benefit of attending? Attendees will have the chance to cover; Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model; Design planning, operating characteristics, statistical analysis; and Applications using the R package RBesT.
Course Cost
Regular Members = £340+VAT Regular Non-Members = £465*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2022.
There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. Including external data in the analysis of clinical trials may increase study power or allow for the reduction of (usually) the control group sample size. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012; Smith et al., 2019), occasionally in phase III trials (French et al., 2012; Viele et al., 2018), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989; Best et al., 2019). This allows adequately powered trials at smaller sample sizes leading to faster trial conduct and exposure of fewer patients to a potentially in-effective control treatment.
In this short course, we will provide a statistical framework to incorporate external information into a trial. During the first part of the course, we will introduce Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model (Neuenschwander et al., 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources. Dynamic borrowing permits to limit the use of historical data when it is incompatible with the data observed within the trial.
In the second part of the course, we will propose a practice session with applications using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. These exercises will enable participants to apply the presented approach themselves. During third and last part of the course, more advanced topics will be detailed such as effective and maximum sample sizes, advanced operating characteristics, and probability of success.
Agenda
Lecture session 1 (morning of Monday 24th January (09:00-12:00))
• Meta-analytic predictive (MAP) model
• Robustification for dynamic borrowing
• Design planning, operating characteristics
• Final analysis Practice sessions (afternoon of Monday 24th, all day Tuesday 25th, all day Wednesday 26th)
• Supervised homework, set-up in a separate forum Lecture session 2 (morning of Thursday 27th January (09:00-12:00))
• Effective sample size, maximum sample size
• Advanced operating characteristics
• Equivalence between MAP and MAC (Meta-analysis combined)
• Probability of success, decision rule
Speaker details
Speaker
Biography
Gaëlle Saint-Hilary
Gaëlle Saint-Hilary is Statistical Methodologist, CEO and founder of the consulting company Saryga (France). Prior to this role, Gaëlle was Statistical Methodologist at Servier until December 2021. With more than 15 years of experience in the pharmaceutical industry (Servier, Novartis) and a strong and long-lasting collaboration with academia, Gaëlle Saint-Hilary is an expert in Bayesian statistics and decision-making support. She has worked at developing novel approaches to improve drug development’s performances, and her main scientific interests are quantitative decision-making, benefit-risk assessment, innovative study designs and historical data.
Sebastian Weber
Sebastian Weber is working as Director in the Department of Advanced Methodology and Data Science at Novartis. He holds a PhD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He has worked extensively on enabling the use of historical (control) information in clinical trials through consulting and working on tools to facilitate the application of historical control information from trial design to analysis. Furthermore, Sebastian has experience in designing Oncology phase I dose-escalation trails and is also involved in pediatric drug development programs, where he applies extrapolation concepts. His research interests include the application of pharmacometrics in statistics, model-based drug development and application of Bayesian methods for drug development.
Disclaimer
PSI is a non-profit organisation run by volunteers. Many of the event organisers and presenters donate their time, while the majority of the event registration cost is spent on administrative support, venue rental / online conferencing, travel costs for the presenter, software licences, and general running of the society. PSI strives to offer high quality courses, but cannot offer a guarantee that the content presented is accurate or fit for your particular needs. Please check if any software is required for this course and ensure you are able to run it prior to registering.
Cancellation and Moderation Terms For cancellations received up to two weeks prior to a PSI event start-date, the event registration fee will be refunded less 25% administrative charge. After this date, no refunds will be possible. A handling fee of 20 GBP per registration will be charged for every registration modification received two weeks prior or less, including a delegate name change.
Careers Meetings
PSI Training Course: Use of Historical Data in Clinical Trials: An Evidence Synthesis Approach
Date: Monday 24th - Thursday 27th January 2022 Time: Lectures 09:00-12:00 on the 24th & 27th. Separate forum setup for practical exercises between the 24th-26th. Speakers: Sebastian Weber and Gaëlle Saint-Hilary
Who is this event intended for? All statisticians working on clinical trials, with a basic knowledge of R. Some notions of Bayesian statistics could be helpful. What is the benefit of attending? Attendees will have the chance to cover; Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model; Design planning, operating characteristics, statistical analysis; and Applications using the R package RBesT.
Course Cost
Regular Members = £340+VAT Regular Non-Members = £465*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2022.
There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. Including external data in the analysis of clinical trials may increase study power or allow for the reduction of (usually) the control group sample size. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012; Smith et al., 2019), occasionally in phase III trials (French et al., 2012; Viele et al., 2018), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989; Best et al., 2019). This allows adequately powered trials at smaller sample sizes leading to faster trial conduct and exposure of fewer patients to a potentially in-effective control treatment.
In this short course, we will provide a statistical framework to incorporate external information into a trial. During the first part of the course, we will introduce Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model (Neuenschwander et al., 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources. Dynamic borrowing permits to limit the use of historical data when it is incompatible with the data observed within the trial.
In the second part of the course, we will propose a practice session with applications using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. These exercises will enable participants to apply the presented approach themselves. During third and last part of the course, more advanced topics will be detailed such as effective and maximum sample sizes, advanced operating characteristics, and probability of success.
Agenda
Lecture session 1 (morning of Monday 24th January (09:00-12:00))
• Meta-analytic predictive (MAP) model
• Robustification for dynamic borrowing
• Design planning, operating characteristics
• Final analysis Practice sessions (afternoon of Monday 24th, all day Tuesday 25th, all day Wednesday 26th)
• Supervised homework, set-up in a separate forum Lecture session 2 (morning of Thursday 27th January (09:00-12:00))
• Effective sample size, maximum sample size
• Advanced operating characteristics
• Equivalence between MAP and MAC (Meta-analysis combined)
• Probability of success, decision rule
Speaker details
Speaker
Biography
Gaëlle Saint-Hilary
Gaëlle Saint-Hilary is Statistical Methodologist, CEO and founder of the consulting company Saryga (France). Prior to this role, Gaëlle was Statistical Methodologist at Servier until December 2021. With more than 15 years of experience in the pharmaceutical industry (Servier, Novartis) and a strong and long-lasting collaboration with academia, Gaëlle Saint-Hilary is an expert in Bayesian statistics and decision-making support. She has worked at developing novel approaches to improve drug development’s performances, and her main scientific interests are quantitative decision-making, benefit-risk assessment, innovative study designs and historical data.
Sebastian Weber
Sebastian Weber is working as Director in the Department of Advanced Methodology and Data Science at Novartis. He holds a PhD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He has worked extensively on enabling the use of historical (control) information in clinical trials through consulting and working on tools to facilitate the application of historical control information from trial design to analysis. Furthermore, Sebastian has experience in designing Oncology phase I dose-escalation trails and is also involved in pediatric drug development programs, where he applies extrapolation concepts. His research interests include the application of pharmacometrics in statistics, model-based drug development and application of Bayesian methods for drug development.
Disclaimer
PSI is a non-profit organisation run by volunteers. Many of the event organisers and presenters donate their time, while the majority of the event registration cost is spent on administrative support, venue rental / online conferencing, travel costs for the presenter, software licences, and general running of the society. PSI strives to offer high quality courses, but cannot offer a guarantee that the content presented is accurate or fit for your particular needs. Please check if any software is required for this course and ensure you are able to run it prior to registering.
Cancellation and Moderation Terms For cancellations received up to two weeks prior to a PSI event start-date, the event registration fee will be refunded less 25% administrative charge. After this date, no refunds will be possible. A handling fee of 20 GBP per registration will be charged for every registration modification received two weeks prior or less, including a delegate name change.
Upcoming Events
Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
PSI Introduction to Industry Training (ITIT) Course - 2024/2025
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
PSI Training Course: Mixed Models and Repeated Measures
This course is presented through lectures and practical sessions using SAS code. It is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
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Analytical cookies
Analytical cookies help us improve our website by collecting and reporting information on its usage.
Vimeo, Inc. is an American video hosting, sharing, services provider, and broadcaster. Vimeo focuses on the delivery of high-definition video across a range of devices.
Cookies used on the site are categorized and below you can read about each category and allow or deny some or all of them. When categories than have been previously allowed are disabled, all cookies assigned to that category will be removed from your browser.
Additionally you can see a list of cookies assigned to each category and detailed information in the cookie declaration.
Some cookies are required to provide core functionality. The website won't function properly without these cookies and they are enabled by default and cannot be disabled.
Necessary cookies
Name
Hostname
Vendor
Expiry
ARRAffinity
.psiweb.org
Session
This cookie is set by websites run on the Windows Azure cloud platform. It is used for load balancing to make sure the visitor page requests are routed to the same server in any browsing session.
ARRAffinitySameSite
.psiweb.org
Session
Used to distribute traffic to the website on several servers in order to optimize response times.
__cf_bm
.vimeo.com
Cloudflare, Inc.
1 hour
The __cf_bm cookie supports Cloudflare Bot Management by managing incoming traffic that matches criteria associated with bots. The cookie does not collect any personal data, and any information collected is subject to one-way encryption.
_cfuvid
.vimeo.com
Session
Used by Cloudflare WAF to distinguish individual users who share the same IP address and apply rate limits
__cf_bm
.glueup.com
Cloudflare, Inc.
1 hour
The __cf_bm cookie supports Cloudflare Bot Management by managing incoming traffic that matches criteria associated with bots. The cookie does not collect any personal data, and any information collected is subject to one-way encryption.
AWSALBTGCORS
psi.glueup.com
7 days
AWS Classic Load Balancer Cookie: Load Balancing Cookie: Used to map the session to the instance. Same value as AWSELB.
PHPSESSID
psi.glueup.com
Session
Cookie generated by applications based on the PHP language. This is a general purpose identifier used to maintain user session variables. It is normally a random generated number, how it is used can be specific to the site, but a good example is maintaining a logged-in status for a user between pages.
Used by CookieHub to store information about whether visitors have given or declined the use of cookie categories used on the site.
Preferences
Preference cookies enables the web site to remember information to customize how the web site looks or behaves for each user. This may include storing selected currency, region, language or color theme.
Preferences
Name
Hostname
Vendor
Expiry
vuid
.vimeo.com
400 days
These cookies are used by the Vimeo video player on websites.
AWSALBCORS
psi.glueup.com
7 days
Amazon Web Services cookie. This cookie enables us to allocate server traffic to make the user experience as smooth as possible. A so-called load balancer is used to determine which server currently has the best availability. The information generated cannot identify you as an individual.
Analytical cookies
Analytical cookies help us improve our website by collecting and reporting information on its usage.
Contains a unique identifier used by Google Analytics to determine that two distinct hits belong to the same user across browsing sessions.
_dd_s
player.vimeo.com
Datadog
1 hour
This cookie is set by Datadog to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).