Dates: Mon.9th, Tues.10th, Thurs.12th & Fri.13th October 2023 Time: 09:00-12:00 BST (each day) Location: Online Speakers: Jonathan Bartlett and James Carpenter (LSHTM)
Who is this event intended for? This course is intended for clinical trial statisticians who are interested in learning more about statistical methods for handling missing data in clinical trial analyses. What is the benefit of attending? By the end of the course participants will be familiar with the key concepts (e.g. missing at random) and statistical methods (e.g. multiple imputation) relevant when estimating treatment effects in trials where some data are missing.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 8th September.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
This course will introduce participants to the key concepts and methods relevant for analysing clinical trials when some data are missing. We will describe missing data assumptions and Rubin’s framework for classifying them, based on missing completely at random, missing at random (MAR), and missing not at random, and what these imply when missingness is due to dropout or the occurrence of intercurrent events. We will describe the use of mixed models and multiple imputation to handle missingness under MAR, and finally discuss methods for conducting missing data sensitivity analyses, including reference based imputation methods.
The course will cover:
Introduction to estimands and missing data in trials; review of missing data assumptions & terminology (e.g. missing at random)
Performing analyses under missing at random for continuous outcome data, using mixed models and multiple imputation (including consideration of retrieved dropout multiple imputation)
Performing analyses under missing at random for binary data, using full conditional specification for multiple imputation with a GEE analysis model
Sensitivity analyses using multiple imputation, including reference based imputation methods
Please note: Each of the above will be presented in a one hour lecture, followed by a two hour interactive computer practical. Computer practicals will be taught using R and so having R or R Studio installed on your personal laptop/computer is required to participate in the practicals.
Speaker details
Speaker
Biography
Jonathan Bartlett LSHTM
Jonathan Bartlett is a Professor in Medical Statistics at the London School of Hygiene & Tropical Medicine.
His research interests are focused around missing data and causal inference methods, and more recently, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University of Bath, and maintains a blog thestatsgeek.com
James Carpenter LSHTM
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, and MRC Investigator in trials methodology at the MRC CTU at UCL.
His principal research interests are coping with missing data in clinical trials and complex hierarchical models, estimands, sensitivity analysis, meta-analysis and novel trial designs.
Dates: Mon.9th, Tues.10th, Thurs.12th & Fri.13th October 2023 Time: 09:00-12:00 BST (each day) Location: Online Speakers: Jonathan Bartlett and James Carpenter (LSHTM)
Who is this event intended for? This course is intended for clinical trial statisticians who are interested in learning more about statistical methods for handling missing data in clinical trial analyses. What is the benefit of attending? By the end of the course participants will be familiar with the key concepts (e.g. missing at random) and statistical methods (e.g. multiple imputation) relevant when estimating treatment effects in trials where some data are missing.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 8th September.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
This course will introduce participants to the key concepts and methods relevant for analysing clinical trials when some data are missing. We will describe missing data assumptions and Rubin’s framework for classifying them, based on missing completely at random, missing at random (MAR), and missing not at random, and what these imply when missingness is due to dropout or the occurrence of intercurrent events. We will describe the use of mixed models and multiple imputation to handle missingness under MAR, and finally discuss methods for conducting missing data sensitivity analyses, including reference based imputation methods.
The course will cover:
Introduction to estimands and missing data in trials; review of missing data assumptions & terminology (e.g. missing at random)
Performing analyses under missing at random for continuous outcome data, using mixed models and multiple imputation (including consideration of retrieved dropout multiple imputation)
Performing analyses under missing at random for binary data, using full conditional specification for multiple imputation with a GEE analysis model
Sensitivity analyses using multiple imputation, including reference based imputation methods
Please note: Each of the above will be presented in a one hour lecture, followed by a two hour interactive computer practical. Computer practicals will be taught using R and so having R or R Studio installed on your personal laptop/computer is required to participate in the practicals.
Speaker details
Speaker
Biography
Jonathan Bartlett LSHTM
Jonathan Bartlett is a Professor in Medical Statistics at the London School of Hygiene & Tropical Medicine.
His research interests are focused around missing data and causal inference methods, and more recently, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University of Bath, and maintains a blog thestatsgeek.com
James Carpenter LSHTM
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, and MRC Investigator in trials methodology at the MRC CTU at UCL.
His principal research interests are coping with missing data in clinical trials and complex hierarchical models, estimands, sensitivity analysis, meta-analysis and novel trial designs.
Dates: Mon.9th, Tues.10th, Thurs.12th & Fri.13th October 2023 Time: 09:00-12:00 BST (each day) Location: Online Speakers: Jonathan Bartlett and James Carpenter (LSHTM)
Who is this event intended for? This course is intended for clinical trial statisticians who are interested in learning more about statistical methods for handling missing data in clinical trial analyses. What is the benefit of attending? By the end of the course participants will be familiar with the key concepts (e.g. missing at random) and statistical methods (e.g. multiple imputation) relevant when estimating treatment effects in trials where some data are missing.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 8th September.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
This course will introduce participants to the key concepts and methods relevant for analysing clinical trials when some data are missing. We will describe missing data assumptions and Rubin’s framework for classifying them, based on missing completely at random, missing at random (MAR), and missing not at random, and what these imply when missingness is due to dropout or the occurrence of intercurrent events. We will describe the use of mixed models and multiple imputation to handle missingness under MAR, and finally discuss methods for conducting missing data sensitivity analyses, including reference based imputation methods.
The course will cover:
Introduction to estimands and missing data in trials; review of missing data assumptions & terminology (e.g. missing at random)
Performing analyses under missing at random for continuous outcome data, using mixed models and multiple imputation (including consideration of retrieved dropout multiple imputation)
Performing analyses under missing at random for binary data, using full conditional specification for multiple imputation with a GEE analysis model
Sensitivity analyses using multiple imputation, including reference based imputation methods
Please note: Each of the above will be presented in a one hour lecture, followed by a two hour interactive computer practical. Computer practicals will be taught using R and so having R or R Studio installed on your personal laptop/computer is required to participate in the practicals.
Speaker details
Speaker
Biography
Jonathan Bartlett LSHTM
Jonathan Bartlett is a Professor in Medical Statistics at the London School of Hygiene & Tropical Medicine.
His research interests are focused around missing data and causal inference methods, and more recently, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University of Bath, and maintains a blog thestatsgeek.com
James Carpenter LSHTM
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, and MRC Investigator in trials methodology at the MRC CTU at UCL.
His principal research interests are coping with missing data in clinical trials and complex hierarchical models, estimands, sensitivity analysis, meta-analysis and novel trial designs.
Dates: Mon.9th, Tues.10th, Thurs.12th & Fri.13th October 2023 Time: 09:00-12:00 BST (each day) Location: Online Speakers: Jonathan Bartlett and James Carpenter (LSHTM)
Who is this event intended for? This course is intended for clinical trial statisticians who are interested in learning more about statistical methods for handling missing data in clinical trial analyses. What is the benefit of attending? By the end of the course participants will be familiar with the key concepts (e.g. missing at random) and statistical methods (e.g. multiple imputation) relevant when estimating treatment effects in trials where some data are missing.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 8th September.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
This course will introduce participants to the key concepts and methods relevant for analysing clinical trials when some data are missing. We will describe missing data assumptions and Rubin’s framework for classifying them, based on missing completely at random, missing at random (MAR), and missing not at random, and what these imply when missingness is due to dropout or the occurrence of intercurrent events. We will describe the use of mixed models and multiple imputation to handle missingness under MAR, and finally discuss methods for conducting missing data sensitivity analyses, including reference based imputation methods.
The course will cover:
Introduction to estimands and missing data in trials; review of missing data assumptions & terminology (e.g. missing at random)
Performing analyses under missing at random for continuous outcome data, using mixed models and multiple imputation (including consideration of retrieved dropout multiple imputation)
Performing analyses under missing at random for binary data, using full conditional specification for multiple imputation with a GEE analysis model
Sensitivity analyses using multiple imputation, including reference based imputation methods
Please note: Each of the above will be presented in a one hour lecture, followed by a two hour interactive computer practical. Computer practicals will be taught using R and so having R or R Studio installed on your personal laptop/computer is required to participate in the practicals.
Speaker details
Speaker
Biography
Jonathan Bartlett LSHTM
Jonathan Bartlett is a Professor in Medical Statistics at the London School of Hygiene & Tropical Medicine.
His research interests are focused around missing data and causal inference methods, and more recently, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University of Bath, and maintains a blog thestatsgeek.com
James Carpenter LSHTM
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, and MRC Investigator in trials methodology at the MRC CTU at UCL.
His principal research interests are coping with missing data in clinical trials and complex hierarchical models, estimands, sensitivity analysis, meta-analysis and novel trial designs.
Dates: Mon.9th, Tues.10th, Thurs.12th & Fri.13th October 2023 Time: 09:00-12:00 BST (each day) Location: Online Speakers: Jonathan Bartlett and James Carpenter (LSHTM)
Who is this event intended for? This course is intended for clinical trial statisticians who are interested in learning more about statistical methods for handling missing data in clinical trial analyses. What is the benefit of attending? By the end of the course participants will be familiar with the key concepts (e.g. missing at random) and statistical methods (e.g. multiple imputation) relevant when estimating treatment effects in trials where some data are missing.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 8th September.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
This course will introduce participants to the key concepts and methods relevant for analysing clinical trials when some data are missing. We will describe missing data assumptions and Rubin’s framework for classifying them, based on missing completely at random, missing at random (MAR), and missing not at random, and what these imply when missingness is due to dropout or the occurrence of intercurrent events. We will describe the use of mixed models and multiple imputation to handle missingness under MAR, and finally discuss methods for conducting missing data sensitivity analyses, including reference based imputation methods.
The course will cover:
Introduction to estimands and missing data in trials; review of missing data assumptions & terminology (e.g. missing at random)
Performing analyses under missing at random for continuous outcome data, using mixed models and multiple imputation (including consideration of retrieved dropout multiple imputation)
Performing analyses under missing at random for binary data, using full conditional specification for multiple imputation with a GEE analysis model
Sensitivity analyses using multiple imputation, including reference based imputation methods
Please note: Each of the above will be presented in a one hour lecture, followed by a two hour interactive computer practical. Computer practicals will be taught using R and so having R or R Studio installed on your personal laptop/computer is required to participate in the practicals.
Speaker details
Speaker
Biography
Jonathan Bartlett LSHTM
Jonathan Bartlett is a Professor in Medical Statistics at the London School of Hygiene & Tropical Medicine.
His research interests are focused around missing data and causal inference methods, and more recently, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University of Bath, and maintains a blog thestatsgeek.com
James Carpenter LSHTM
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, and MRC Investigator in trials methodology at the MRC CTU at UCL.
His principal research interests are coping with missing data in clinical trials and complex hierarchical models, estimands, sensitivity analysis, meta-analysis and novel trial designs.
Dates: Mon.9th, Tues.10th, Thurs.12th & Fri.13th October 2023 Time: 09:00-12:00 BST (each day) Location: Online Speakers: Jonathan Bartlett and James Carpenter (LSHTM)
Who is this event intended for? This course is intended for clinical trial statisticians who are interested in learning more about statistical methods for handling missing data in clinical trial analyses. What is the benefit of attending? By the end of the course participants will be familiar with the key concepts (e.g. missing at random) and statistical methods (e.g. multiple imputation) relevant when estimating treatment effects in trials where some data are missing.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 8th September.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
This course will introduce participants to the key concepts and methods relevant for analysing clinical trials when some data are missing. We will describe missing data assumptions and Rubin’s framework for classifying them, based on missing completely at random, missing at random (MAR), and missing not at random, and what these imply when missingness is due to dropout or the occurrence of intercurrent events. We will describe the use of mixed models and multiple imputation to handle missingness under MAR, and finally discuss methods for conducting missing data sensitivity analyses, including reference based imputation methods.
The course will cover:
Introduction to estimands and missing data in trials; review of missing data assumptions & terminology (e.g. missing at random)
Performing analyses under missing at random for continuous outcome data, using mixed models and multiple imputation (including consideration of retrieved dropout multiple imputation)
Performing analyses under missing at random for binary data, using full conditional specification for multiple imputation with a GEE analysis model
Sensitivity analyses using multiple imputation, including reference based imputation methods
Please note: Each of the above will be presented in a one hour lecture, followed by a two hour interactive computer practical. Computer practicals will be taught using R and so having R or R Studio installed on your personal laptop/computer is required to participate in the practicals.
Speaker details
Speaker
Biography
Jonathan Bartlett LSHTM
Jonathan Bartlett is a Professor in Medical Statistics at the London School of Hygiene & Tropical Medicine.
His research interests are focused around missing data and causal inference methods, and more recently, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University of Bath, and maintains a blog thestatsgeek.com
James Carpenter LSHTM
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, and MRC Investigator in trials methodology at the MRC CTU at UCL.
His principal research interests are coping with missing data in clinical trials and complex hierarchical models, estimands, sensitivity analysis, meta-analysis and novel trial designs.
Upcoming Events
PSI Webinar: Development of Gene Therapies: Strategic, Scientific, Regulatory and Access Considerations
This webinar will cover the history of cell/gene therapy, major regulatory advances, the role of quantitative scientists in drug development of these novel therapeutics, and discuss opportunities for innovation and product advancement.
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.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
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: Regulatory Guidelines for Statisticians
This 2-day course is designed to provide a comprehensive understanding of the regulatory guidelines affecting statisticians in the pharmaceutical industry, including the latest updates in the field. The course will cover key International Council for Harmonisation (ICH) guidelines and other key regional regulatory agency documents.
In this event, we’ll start with a general introduction to hierarchical composite endpoints and an overview of common analysis methods including win ratio, win odds, and net benefit. Then, the practical considerations will be illustrated through case studies from clinical trials in heart failure and chronic kidney disease. We conclude with a discussion and a Q&A.
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Virtual Control Groups in Toxicity Studies
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.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
Statisticians in the Pharmaceutical Industry Executive Office: c/o MCI UK Ltd | Unit 24/22 South | Building 4000 | Langstone Park| Langstone Road | Havant | PO9 1SA | UK