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
Date: The Second Wednesday of every Month
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 Training Course: Introduction to Bayesian Methods
Dates:
Module 1 - Wednesday 20th November
Module 2 - Tuesday 26th November
Module 3 - Thursday 28th November
This interactive and hands on online course, aimed at beginner Bayesians who have little or no experience in Bayesian statistics, is split into 3 modules. Module 1 introduces Bayesian concepts and looks at how Bayes has been used recently in the Pharmaceutical Industry, and participants will undertake their own data collection exercise in order to analyse a simple Bayesian trial. Module 2 takes a look at the concepts of Bayesian statistics and key terminology and provides a chance to analyse some data using a simple Bayesian approach. Module 3 takes a further look at the use of Bayesian statistics in drug development and regulatory requirements and digs deeper into analysis issues.
Date: 20 October 2024
This event is aimed at students with an interest in the field of Medical Statistics, for example within pharmaceuticals, healthcare and/or medical research.
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Virtual Control Groups in Toxicity Studies
Date: Re-scheduled to Tuesday 26th November 2024
Lea Vaas will present how replacement of concurrent control animals by Virtual Control Groups (VCGs) in systemic toxicity studies may help in contributing to the 3R's principle of animal experimentation: Reduce, Refine, Replace.
PSI Book Club Webinar: 7 Habits You Wish You Had (But Probably Don't)
Date: Wednesday 27th November 2024
The 7 Habits of Highly Effective People has had a profound impact on millions of people around the world. The book focuses on cultivating character and refining personality. Its principles are not only transformative for personal growth but also serve as powerful tools for professional development.
Over the past few months, members of the PSI Book Club have been exploring these transformative principles through focused discussions and real-world applications. These principles aren’t just theories—they are actionable strategies that can be integrated into your daily life to drive meaningful change.
In this upcoming session, we’ll bring these discussions to a broader audience. Together, we’ll explore how to practically apply the 7 Habits in our professional lives, share key insights from our book club sessions, and reflect on how these timeless principles can enhance both our personal and professional journeys.
Date: Tuesday 10th December 2024
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
Date: Thursday 12th December 2024
Chaired by Jenny Devenport, join us to hear Andy Grieve and Zhiwei Zhang discuss their recent work on group sequential designs.
PSI Introduction to Industry Training (ITIT) Course - 2024/2025
Date: October 2024 - July 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.