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 Book Club: Change: How organisations achieve hard-to-imagine results in uncertain and volatile times
Organizations have to adapt to the transforming landscape of our industry to ensure they continue to be successful in the future. Many of us are feeling the impact of organizational change. By reading John P Kotter’s book we can understand about organizational change and learn how to thrive, rather than just survive, through change.
Change, by John P Kotter (and his team), is a summary of all that he has learned over his decades of research and leading change. His book describes why many current approaches to change are inadequate and explains why new solutions need to give people a voice and a role in a new, change-embracing organization.
Develop your understanding of organisational change and become empowered to be part of your organisation’s change, by reading Change by John P Kotter and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply knowledge from the book in-between sessions.
Our monthly webinar series allows attendees to gain practical knowledge and skills in open-source coding and tools, with a focus on applications in the pharmaceutical industry.
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 Book Club: Another Door Opens – Book Club Special Event
This is a Book Club Special Event in response to the changes in our industry and as a supportive move to create community and connection for those navigating redundancy and uncertainty. Read the book in advance of the book club session then join the zoom call to discuss ideas. There will be breakout groups to connect with others, exchange experiences of how the book has helped, and offer support.
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 Training Course: Propensity Scores: Practical Application in Non-randomised Studies
The course will introduce the topic of propensity scores and the use of external data. Covering the topics of matching and weighting as well as more advance topics of high dimension propensity scores, multi-valued treatments, double robustness and time-varying scenarios. There will be the opportunity to participate in some hands on practical exercises in R.
Making Estimands Work - Practical Approaches with Implementation in Data Standards
This event will discuss the impact of the estimand framework on data flow and data handling for analysis datasets (ADaM). What are the considerations to produce data sets suitable for estimation of estimands and traceability of the occurrence of intercurrent events. This webinar will include tips for best practice, based on the White paper Implementation of ICH E9(R1) Estimands Framework using Data Standards.
PSI/EFSPI Biomarker SIG Webinar: Statistical Methods for ctDNA Data
This webinar will describe what ctDNA is and what the key challenges are for analysing it. We will then summarise and review methods that were published over the last few years for analysing ctDNA data.
This two-afternoon virtual course provides a practical introduction to adaptive clinical trials, focusing on the concepts, applications, and regulatory principles outlined in ICH E20 through real-world examples and case studies.
PSI Careers - MEDMathS: Medicine Empowered by Data, Maths and Statistics
Date: Wednesday 4th November 2026
A careers talk about medical statistics and how it plays a crucial role in developing new medicines. Learn about the field of medical statistics and how it plays a crucial role in developing groundbreaking new medicines, vaccines and healthcare products.
Date: 18 November 2026
This is an excellent opportunity for students to find out more about the field of medical statistics, talk to people from different organisations and make contacts for the future. All students currently studying for a mathematics or statistics-related BSc, MSc or PhD are invited to attend, and we welcome interest from exhibitors too.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
In this vital role you will generate evidence to meet Health Technology Assessment (HTA) requirements, while ensuring that the analyses performed meet required standards and are robust and valid.
Bristol Myers Squibb - Director, Statistical Methodology and Innovation
Lead the development of innovative statistical methods, provides expert consulting, oversees tools and software, and mentors team members while collaborating cross-functionally to address complex drug development challenges.
Phastar – Associate Director, Statistics – Real World Evidence (RWE)
As an Associate Director, Statistics (RWE) you will focus on supporting one of our key pharmaceutical clients within our FSP function. This will be a hands-on technical role, with oversight responsibilities and cross-functional exposure.
Phastar – Principal Statistician – Real World Evidence (RWE)
We are seeking an experienced Statistician to join our FSP team to support one of our fantastic and well-known global pharmaceutical clients, at Principal level.
We are seeking an experienced Statistician to join our FSP team to support one of our fantastic and well-known global pharmaceutical clients, at either Senior or Principal levels.
This position is deal for a statistician who values ownership, collaboration, and using data to enable confident development decisions and to support regulatory submissions.
