Dates:
Session 1 - Monday 14th September 2026
Session 2 - Tuesday 15th September 2026
Session 3 - Monday 21st September 2026
Session 4 - Tuesday 22nd September 2026
Time: 09:00 - 13:00 BST
Location: Online via Zoom
Who is this event intended for?
Biostatisticians looking for an introduction to propensity scores
What is the benefit of attending?
Gain an understanding of both basic and more advanced topics
Gain hands on experience with practical exercises in R
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.
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 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 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.
Early Bird PSI Members: £320 +VAT
PSI Members: £360 +VAT
Early Bird Non-PSI Members: £430 +VAT
Non-PSI Members: £470 +VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2026.
Early Bird registration closes on Friday 28th August.
To register for this event, please click here.
Session 1
Session 2
Session 3
Session 4
Speaker | Biography |
Elizabeth Williamson, Professor of Biostatistics and Health Data Science, 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, Associate Professor in Medical Statistics, 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, Assistant Professor in Medical Statistics, | John Tazare is an Assistant Professor in Medical Statistics 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 target trial emulation in non-randomised settings. |
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