PSI Training Course: Propensity Scores - practical application in non-randomised studies

PSI Medical Statistics Careers Event

This event is aimed at students with an interest in the field of Medical Statistics, for example within pharmaceuticals, healthcare and/or medical research.

PSI Career Young Virtual Meet (Q4 2023)

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 Webinar(s): Causal inference in Clinical Trials

Over the course of two sessions, a panel of 8 esteemed speakers will give an introduction to the topic, followed by a presentation of case studies & interactive panel discussion.

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PSI VisSIG Wonderful Wednesday Webinar Series (2023)

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: R for Clinical Trial Statisticians

This course consists of 5 x 2 hour training sessions run over 5 weeks. For those who cannot attend the date and time provided, there will be a recording provided and a follow-up 1 hour Q and A session. Participation will provide access to extensive course materials in the form of a “bookdown” site. However, this is designed as a hands-on, interactive course. Both you and the trainers will work entirely within the RStudio Cloud development environment, which will remain accessible between virtual sessions in order to further explore and cement your understanding.

PSI Pre-Clinical SIG Workshop

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PSI Mentoring 2024

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.

Session	Topic
Session 1	Introduction to propensity scores Propensity score methods Practical exercise using R
Session 2	Estimating the propensity score Propensity scores for multi-valued treatments Practical exercise using R
Session 3	Handling missing data High dimensional propensity scores Practical exercise using R
Session 4	Outcome regression and double robustness Time-varying scenarios Practical exercise using R

Speaker	Biography
Elizabeth Williamson LSHTM	Elizabeth Williamson is a Professor of Biostatistics and Health Data Science at the London School of Hygiene and Tropical Medicine. Her research focuses on improving statistical methods for using electronic health record data for research. Elizabeth has a long-term interest in propensity scores, beginning with her PhD in 2003-7 which explored issues around variance estimation, moving on to handling missing data within propensity scores and, more recently, exploring high-dimensional confounding within propensity score analysis.
Clemence Leyrat LSHTM	Clemence Leyrat is an Associate Professor in Medical Statistics at the London School of Hygiene and Tropical Medicine. Since completing her PhD in 2014 on the use of propensity scores in cluster randomised trials, most of her research has focused on causal inference methods for the analysis of observational studies, including trial emulation. More recently, she has been investigating the properties of propensity score weighting in longitudinal settings and in the presence of clustering by hospital.
John Tazare LSHTM	John Tazare is an Assistant Professor in Statistical Pharmacoepidemiology at the London School of Hygiene and Tropical Medicine. In 2021, John completed a PhD surrounding the use of high-dimensional propensity scores in UK electronic health records. John’s current research areas include the use of time-conditional propensity scores in prevalent new user designs and applications of causal inference methods (for example, clone-censor weighting approaches) for target trial emulation in non-randomised settings.

Session	Topic
Session 1	Introduction to propensity scores Propensity score methods Practical exercise using R
Session 2	Estimating the propensity score Propensity scores for multi-valued treatments Practical exercise using R
Session 3	Handling missing data High dimensional propensity scores Practical exercise using R
Session 4	Outcome regression and double robustness Time-varying scenarios Practical exercise using R

Speaker	Biography
Elizabeth Williamson LSHTM	Elizabeth Williamson is a Professor of Biostatistics and Health Data Science at the London School of Hygiene and Tropical Medicine. Her research focuses on improving statistical methods for using electronic health record data for research. Elizabeth has a long-term interest in propensity scores, beginning with her PhD in 2003-7 which explored issues around variance estimation, moving on to handling missing data within propensity scores and, more recently, exploring high-dimensional confounding within propensity score analysis.
Clemence Leyrat LSHTM	Clemence Leyrat is an Associate Professor in Medical Statistics at the London School of Hygiene and Tropical Medicine. Since completing her PhD in 2014 on the use of propensity scores in cluster randomised trials, most of her research has focused on causal inference methods for the analysis of observational studies, including trial emulation. More recently, she has been investigating the properties of propensity score weighting in longitudinal settings and in the presence of clustering by hospital.
John Tazare LSHTM	John Tazare is an Assistant Professor in Statistical Pharmacoepidemiology at the London School of Hygiene and Tropical Medicine. In 2021, John completed a PhD surrounding the use of high-dimensional propensity scores in UK electronic health records. John’s current research areas include the use of time-conditional propensity scores in prevalent new user designs and applications of causal inference methods (for example, clone-censor weighting approaches) for target trial emulation in non-randomised settings.

Session	Topic
Session 1	Introduction to propensity scores Propensity score methods Practical exercise using R
Session 2	Estimating the propensity score Propensity scores for multi-valued treatments Practical exercise using R
Session 3	Handling missing data High dimensional propensity scores Practical exercise using R
Session 4	Outcome regression and double robustness Time-varying scenarios Practical exercise using R

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