Dates & Times:
Tuesday 4th February 2025: 08:30 - 13:00 BST
Wednesday 5th February 2025: 08:30 - 13:00 BST
Thursday 6th February 2025: 08:30 - 13:00 BST
Friday 7th February 2025: 08:30 - 13:00 BST
Location:
Online via Zoom Speakers: James Carpenter (LSHTM), Linda Sharples (LSHTM)
Who is this event intended for? This course is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models and want to further their knowledge of repeated measures and other clustered data. What is the benefit of attending? By the end of the course attendees will know how to analyse repeated measures of patients through time or other clustered data in randomised clinical trials and associated observational studies. Attendees will develop their knowledge on conditional models for continuous hierarchical and longitudinal data, GEE and discrete models. Practical exercises will allow hands-on experience when working with this type of data and presenters will be available during the course to answer any questions.
Cost
Early Bird PSI Members = £320+VAT PSI Members = £360+VAT
Early Bird Non-Members = £430+VAT Non-Members = £470+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2025.
Repeated measures of patients through time and other clustered data are common in randomised clinical trials and associated observational studies. Measurements taken from the same patient (or from the same cluster) are likely to be correlated, so that the assumption that all responses will be identically distributed and independent from each other will not hold. Ignoring within-cluster correlation will result in bias in the estimate of the treatment effect standard error and therefore, incorrect confidence intervals and hypothesis tests. In some situations it can also result in bias in the treatment estimate itself. Using a range of worked examples, this course will explain how to analyse repeated measures and other clustered data, with an emphasis on estimating treatment effects using the appropriate covariance structure between measurements.
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.
Topics covered include:
• Conditional models for continuous hierarchical data
• Conditional models for continuous longitudinal data
• Marginal models (GEE) for continuous longitudinal data
• Discrete data
Speaker details
Speaker
Biography
James Carpenter
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, with a 50% secondment to the MRC Clinical Trials Unit at UCL, where he is an MRC Investigator in Trials Methodology.
He has long-standing interests in missing data methodology (particularly multiple imputation) and longitudinal modelling; last year he co-authored the second edition of Multiple imputation and its Application (Wiley). More recently, he became the senior statistician in the ACORD collaboration, focussed on developing, running and reporting multi-arm multi-stage trials in neurodegenerative diseases.
Linda Sharples
After finishing a PhD in Bayesian Methods for Hierarchical Data and a short post-doctoral lectureship in Medical Statistics, Linda joined the MRC Biostatistics Unit in Cambridge. At Cambridge she led a programme of research in the evaluation of new health technologies using both experimental and observational designs. The work was applied to interventions to treat heart and lung diseases, particularly transplantation and other surgery. Methodological contributions have focussed on longitudinal data and the synthesis of trial and observational data in decision models. She was appointed Professor of Statistics in the Clinical Trials Research Unit at Leeds University in 2013, but could not resist relocating to the LSHTM when the opportunity arose in 2017. Her current interests focus on developing clinical prediction models for early diagnosis of cancers with non-specific symptoms and use of electronic health records for evaluation of service provision for (among others) prostate cancer.
Scientific Meetings
PSI Training Course: Mixed Models and Repeated Measures
Dates & Times:
Tuesday 4th February 2025: 08:30 - 13:00 BST
Wednesday 5th February 2025: 08:30 - 13:00 BST
Thursday 6th February 2025: 08:30 - 13:00 BST
Friday 7th February 2025: 08:30 - 13:00 BST
Location:
Online via Zoom Speakers: James Carpenter (LSHTM), Linda Sharples (LSHTM)
Who is this event intended for? This course is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models and want to further their knowledge of repeated measures and other clustered data. What is the benefit of attending? By the end of the course attendees will know how to analyse repeated measures of patients through time or other clustered data in randomised clinical trials and associated observational studies. Attendees will develop their knowledge on conditional models for continuous hierarchical and longitudinal data, GEE and discrete models. Practical exercises will allow hands-on experience when working with this type of data and presenters will be available during the course to answer any questions.
Cost
Early Bird PSI Members = £320+VAT PSI Members = £360+VAT
Early Bird Non-Members = £430+VAT Non-Members = £470+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2025.
Repeated measures of patients through time and other clustered data are common in randomised clinical trials and associated observational studies. Measurements taken from the same patient (or from the same cluster) are likely to be correlated, so that the assumption that all responses will be identically distributed and independent from each other will not hold. Ignoring within-cluster correlation will result in bias in the estimate of the treatment effect standard error and therefore, incorrect confidence intervals and hypothesis tests. In some situations it can also result in bias in the treatment estimate itself. Using a range of worked examples, this course will explain how to analyse repeated measures and other clustered data, with an emphasis on estimating treatment effects using the appropriate covariance structure between measurements.
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.
Topics covered include:
• Conditional models for continuous hierarchical data
• Conditional models for continuous longitudinal data
• Marginal models (GEE) for continuous longitudinal data
• Discrete data
Speaker details
Speaker
Biography
James Carpenter
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, with a 50% secondment to the MRC Clinical Trials Unit at UCL, where he is an MRC Investigator in Trials Methodology.
He has long-standing interests in missing data methodology (particularly multiple imputation) and longitudinal modelling; last year he co-authored the second edition of Multiple imputation and its Application (Wiley). More recently, he became the senior statistician in the ACORD collaboration, focussed on developing, running and reporting multi-arm multi-stage trials in neurodegenerative diseases.
Linda Sharples
After finishing a PhD in Bayesian Methods for Hierarchical Data and a short post-doctoral lectureship in Medical Statistics, Linda joined the MRC Biostatistics Unit in Cambridge. At Cambridge she led a programme of research in the evaluation of new health technologies using both experimental and observational designs. The work was applied to interventions to treat heart and lung diseases, particularly transplantation and other surgery. Methodological contributions have focussed on longitudinal data and the synthesis of trial and observational data in decision models. She was appointed Professor of Statistics in the Clinical Trials Research Unit at Leeds University in 2013, but could not resist relocating to the LSHTM when the opportunity arose in 2017. Her current interests focus on developing clinical prediction models for early diagnosis of cancers with non-specific symptoms and use of electronic health records for evaluation of service provision for (among others) prostate cancer.
Training Courses
PSI Training Course: Mixed Models and Repeated Measures
Dates & Times:
Tuesday 4th February 2025: 08:30 - 13:00 BST
Wednesday 5th February 2025: 08:30 - 13:00 BST
Thursday 6th February 2025: 08:30 - 13:00 BST
Friday 7th February 2025: 08:30 - 13:00 BST
Location:
Online via Zoom Speakers: James Carpenter (LSHTM), Linda Sharples (LSHTM)
Who is this event intended for? This course is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models and want to further their knowledge of repeated measures and other clustered data. What is the benefit of attending? By the end of the course attendees will know how to analyse repeated measures of patients through time or other clustered data in randomised clinical trials and associated observational studies. Attendees will develop their knowledge on conditional models for continuous hierarchical and longitudinal data, GEE and discrete models. Practical exercises will allow hands-on experience when working with this type of data and presenters will be available during the course to answer any questions.
Cost
Early Bird PSI Members = £320+VAT PSI Members = £360+VAT
Early Bird Non-Members = £430+VAT Non-Members = £470+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2025.
Repeated measures of patients through time and other clustered data are common in randomised clinical trials and associated observational studies. Measurements taken from the same patient (or from the same cluster) are likely to be correlated, so that the assumption that all responses will be identically distributed and independent from each other will not hold. Ignoring within-cluster correlation will result in bias in the estimate of the treatment effect standard error and therefore, incorrect confidence intervals and hypothesis tests. In some situations it can also result in bias in the treatment estimate itself. Using a range of worked examples, this course will explain how to analyse repeated measures and other clustered data, with an emphasis on estimating treatment effects using the appropriate covariance structure between measurements.
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.
Topics covered include:
• Conditional models for continuous hierarchical data
• Conditional models for continuous longitudinal data
• Marginal models (GEE) for continuous longitudinal data
• Discrete data
Speaker details
Speaker
Biography
James Carpenter
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, with a 50% secondment to the MRC Clinical Trials Unit at UCL, where he is an MRC Investigator in Trials Methodology.
He has long-standing interests in missing data methodology (particularly multiple imputation) and longitudinal modelling; last year he co-authored the second edition of Multiple imputation and its Application (Wiley). More recently, he became the senior statistician in the ACORD collaboration, focussed on developing, running and reporting multi-arm multi-stage trials in neurodegenerative diseases.
Linda Sharples
After finishing a PhD in Bayesian Methods for Hierarchical Data and a short post-doctoral lectureship in Medical Statistics, Linda joined the MRC Biostatistics Unit in Cambridge. At Cambridge she led a programme of research in the evaluation of new health technologies using both experimental and observational designs. The work was applied to interventions to treat heart and lung diseases, particularly transplantation and other surgery. Methodological contributions have focussed on longitudinal data and the synthesis of trial and observational data in decision models. She was appointed Professor of Statistics in the Clinical Trials Research Unit at Leeds University in 2013, but could not resist relocating to the LSHTM when the opportunity arose in 2017. Her current interests focus on developing clinical prediction models for early diagnosis of cancers with non-specific symptoms and use of electronic health records for evaluation of service provision for (among others) prostate cancer.
Journal Club
PSI Training Course: Mixed Models and Repeated Measures
Dates & Times:
Tuesday 4th February 2025: 08:30 - 13:00 BST
Wednesday 5th February 2025: 08:30 - 13:00 BST
Thursday 6th February 2025: 08:30 - 13:00 BST
Friday 7th February 2025: 08:30 - 13:00 BST
Location:
Online via Zoom Speakers: James Carpenter (LSHTM), Linda Sharples (LSHTM)
Who is this event intended for? This course is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models and want to further their knowledge of repeated measures and other clustered data. What is the benefit of attending? By the end of the course attendees will know how to analyse repeated measures of patients through time or other clustered data in randomised clinical trials and associated observational studies. Attendees will develop their knowledge on conditional models for continuous hierarchical and longitudinal data, GEE and discrete models. Practical exercises will allow hands-on experience when working with this type of data and presenters will be available during the course to answer any questions.
Cost
Early Bird PSI Members = £320+VAT PSI Members = £360+VAT
Early Bird Non-Members = £430+VAT Non-Members = £470+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2025.
Repeated measures of patients through time and other clustered data are common in randomised clinical trials and associated observational studies. Measurements taken from the same patient (or from the same cluster) are likely to be correlated, so that the assumption that all responses will be identically distributed and independent from each other will not hold. Ignoring within-cluster correlation will result in bias in the estimate of the treatment effect standard error and therefore, incorrect confidence intervals and hypothesis tests. In some situations it can also result in bias in the treatment estimate itself. Using a range of worked examples, this course will explain how to analyse repeated measures and other clustered data, with an emphasis on estimating treatment effects using the appropriate covariance structure between measurements.
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.
Topics covered include:
• Conditional models for continuous hierarchical data
• Conditional models for continuous longitudinal data
• Marginal models (GEE) for continuous longitudinal data
• Discrete data
Speaker details
Speaker
Biography
James Carpenter
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, with a 50% secondment to the MRC Clinical Trials Unit at UCL, where he is an MRC Investigator in Trials Methodology.
He has long-standing interests in missing data methodology (particularly multiple imputation) and longitudinal modelling; last year he co-authored the second edition of Multiple imputation and its Application (Wiley). More recently, he became the senior statistician in the ACORD collaboration, focussed on developing, running and reporting multi-arm multi-stage trials in neurodegenerative diseases.
Linda Sharples
After finishing a PhD in Bayesian Methods for Hierarchical Data and a short post-doctoral lectureship in Medical Statistics, Linda joined the MRC Biostatistics Unit in Cambridge. At Cambridge she led a programme of research in the evaluation of new health technologies using both experimental and observational designs. The work was applied to interventions to treat heart and lung diseases, particularly transplantation and other surgery. Methodological contributions have focussed on longitudinal data and the synthesis of trial and observational data in decision models. She was appointed Professor of Statistics in the Clinical Trials Research Unit at Leeds University in 2013, but could not resist relocating to the LSHTM when the opportunity arose in 2017. Her current interests focus on developing clinical prediction models for early diagnosis of cancers with non-specific symptoms and use of electronic health records for evaluation of service provision for (among others) prostate cancer.
Webinars
PSI Training Course: Mixed Models and Repeated Measures
Dates & Times:
Tuesday 4th February 2025: 08:30 - 13:00 BST
Wednesday 5th February 2025: 08:30 - 13:00 BST
Thursday 6th February 2025: 08:30 - 13:00 BST
Friday 7th February 2025: 08:30 - 13:00 BST
Location:
Online via Zoom Speakers: James Carpenter (LSHTM), Linda Sharples (LSHTM)
Who is this event intended for? This course is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models and want to further their knowledge of repeated measures and other clustered data. What is the benefit of attending? By the end of the course attendees will know how to analyse repeated measures of patients through time or other clustered data in randomised clinical trials and associated observational studies. Attendees will develop their knowledge on conditional models for continuous hierarchical and longitudinal data, GEE and discrete models. Practical exercises will allow hands-on experience when working with this type of data and presenters will be available during the course to answer any questions.
Cost
Early Bird PSI Members = £320+VAT PSI Members = £360+VAT
Early Bird Non-Members = £430+VAT Non-Members = £470+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2025.
Repeated measures of patients through time and other clustered data are common in randomised clinical trials and associated observational studies. Measurements taken from the same patient (or from the same cluster) are likely to be correlated, so that the assumption that all responses will be identically distributed and independent from each other will not hold. Ignoring within-cluster correlation will result in bias in the estimate of the treatment effect standard error and therefore, incorrect confidence intervals and hypothesis tests. In some situations it can also result in bias in the treatment estimate itself. Using a range of worked examples, this course will explain how to analyse repeated measures and other clustered data, with an emphasis on estimating treatment effects using the appropriate covariance structure between measurements.
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.
Topics covered include:
• Conditional models for continuous hierarchical data
• Conditional models for continuous longitudinal data
• Marginal models (GEE) for continuous longitudinal data
• Discrete data
Speaker details
Speaker
Biography
James Carpenter
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, with a 50% secondment to the MRC Clinical Trials Unit at UCL, where he is an MRC Investigator in Trials Methodology.
He has long-standing interests in missing data methodology (particularly multiple imputation) and longitudinal modelling; last year he co-authored the second edition of Multiple imputation and its Application (Wiley). More recently, he became the senior statistician in the ACORD collaboration, focussed on developing, running and reporting multi-arm multi-stage trials in neurodegenerative diseases.
Linda Sharples
After finishing a PhD in Bayesian Methods for Hierarchical Data and a short post-doctoral lectureship in Medical Statistics, Linda joined the MRC Biostatistics Unit in Cambridge. At Cambridge she led a programme of research in the evaluation of new health technologies using both experimental and observational designs. The work was applied to interventions to treat heart and lung diseases, particularly transplantation and other surgery. Methodological contributions have focussed on longitudinal data and the synthesis of trial and observational data in decision models. She was appointed Professor of Statistics in the Clinical Trials Research Unit at Leeds University in 2013, but could not resist relocating to the LSHTM when the opportunity arose in 2017. Her current interests focus on developing clinical prediction models for early diagnosis of cancers with non-specific symptoms and use of electronic health records for evaluation of service provision for (among others) prostate cancer.
Careers Meetings
PSI Training Course: Mixed Models and Repeated Measures
Dates & Times:
Tuesday 4th February 2025: 08:30 - 13:00 BST
Wednesday 5th February 2025: 08:30 - 13:00 BST
Thursday 6th February 2025: 08:30 - 13:00 BST
Friday 7th February 2025: 08:30 - 13:00 BST
Location:
Online via Zoom Speakers: James Carpenter (LSHTM), Linda Sharples (LSHTM)
Who is this event intended for? This course is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models and want to further their knowledge of repeated measures and other clustered data. What is the benefit of attending? By the end of the course attendees will know how to analyse repeated measures of patients through time or other clustered data in randomised clinical trials and associated observational studies. Attendees will develop their knowledge on conditional models for continuous hierarchical and longitudinal data, GEE and discrete models. Practical exercises will allow hands-on experience when working with this type of data and presenters will be available during the course to answer any questions.
Cost
Early Bird PSI Members = £320+VAT PSI Members = £360+VAT
Early Bird Non-Members = £430+VAT Non-Members = £470+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2025.
Repeated measures of patients through time and other clustered data are common in randomised clinical trials and associated observational studies. Measurements taken from the same patient (or from the same cluster) are likely to be correlated, so that the assumption that all responses will be identically distributed and independent from each other will not hold. Ignoring within-cluster correlation will result in bias in the estimate of the treatment effect standard error and therefore, incorrect confidence intervals and hypothesis tests. In some situations it can also result in bias in the treatment estimate itself. Using a range of worked examples, this course will explain how to analyse repeated measures and other clustered data, with an emphasis on estimating treatment effects using the appropriate covariance structure between measurements.
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.
Topics covered include:
• Conditional models for continuous hierarchical data
• Conditional models for continuous longitudinal data
• Marginal models (GEE) for continuous longitudinal data
• Discrete data
Speaker details
Speaker
Biography
James Carpenter
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, with a 50% secondment to the MRC Clinical Trials Unit at UCL, where he is an MRC Investigator in Trials Methodology.
He has long-standing interests in missing data methodology (particularly multiple imputation) and longitudinal modelling; last year he co-authored the second edition of Multiple imputation and its Application (Wiley). More recently, he became the senior statistician in the ACORD collaboration, focussed on developing, running and reporting multi-arm multi-stage trials in neurodegenerative diseases.
Linda Sharples
After finishing a PhD in Bayesian Methods for Hierarchical Data and a short post-doctoral lectureship in Medical Statistics, Linda joined the MRC Biostatistics Unit in Cambridge. At Cambridge she led a programme of research in the evaluation of new health technologies using both experimental and observational designs. The work was applied to interventions to treat heart and lung diseases, particularly transplantation and other surgery. Methodological contributions have focussed on longitudinal data and the synthesis of trial and observational data in decision models. She was appointed Professor of Statistics in the Clinical Trials Research Unit at Leeds University in 2013, but could not resist relocating to the LSHTM when the opportunity arose in 2017. Her current interests focus on developing clinical prediction models for early diagnosis of cancers with non-specific symptoms and use of electronic health records for evaluation of service provision for (among others) prostate cancer.
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.
Topic: R Package Basics.
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 month’s session, “R Package Basics,” will introduce the fundamentals of working with R packages—covering how to install, load, and manage them effectively to support data analysis and reproducible research. The session will provide a solid starting point, clarify common misconceptions, and offer valuable resources for continued learning.
Date: Ongoing 6 month cycle beginning late April/early May 2026
Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.
PSI Book Club Lunch and Learn: Communicating with Clarity and Confidence
If you have read Ros Atkins’ book The Art of Explanation or want to listen to the BBC’s ‘Communicator in Chief’, you are invited to join the PSI Book Club Lunch and Learn, to discuss the content and application with the author, Ros Atkins. Having written the book within the context of the news industry, Ros is keen to hear how we have applied the ideas as statisticians within drug development and clinical trials. There will be dedicated time during the webinar to ASK THE AUTHOR any questions – don’t miss out on this exclusive PSI Book Club event!
Haven’t read the book yet? Pick up a copy today and join us.
Explanation - identifying and communicating what we want to say - is described as an art, in the title of his book. However, the creativity comes from Ros’ discernment in identifying and describing a clear step-by-step process to follow and practice. Readers can learn Ros’ rules, developed and polished throughout his career as a journalist, to help communicate complex written or spoken information clearly.
PSI Training Course: Effective Leadership – the keys to growing your leadership capabilities
This course will consist of three online half-day workshops. The first will be aimed at building trust, the backbone of leadership and a key to becoming effective. This is key to building a solid foundation.
The second will be on improving communication as a technical leader. This workshop will focus on communication strategies for different stakeholders and will involve tips on effective communication and how to develop the skills of active listening, coaching and what improv can teach us about good communication.
The final workshop will bring these two components together to help leaders become more influential. This will also focus on how to use Steven Covey’s 7-Habits, in particular Habits 4, 5 and 6, which are called the habits of communication.
The workshops will be interactive, allowing you to practice the concepts discussed. There will be plenty of time for questions and discussion. There will also be reflective time where you can think about what you are learning and how you might experiment with it.