Registration is now open for our annual series of workshops in February. Held online this year, in the form of a half-day session each day, we hope to (virtually) see you there!
Week One (15-19 February): Multiple Imputation
- Introduction to Multiple Imputation for Missing Data (15th and 16th February)
- Sensitivity analyses to departures from the ‘missing at random’ assumption (17th February)
- Multiple Imputation for longitudinal data (18th and 19th February)
Presented by: Dr Cattram Nguyen (Convenor), Prof Katherine Lee, Prof Julie Simpson, Prof John Carlin, Dr Margarita Moreno-Betancur, Dr Rheanna Mainzer, Dr Ghazaleh Dashti, Dr Anurika De Silva, Ms Rushani Wijesuriya, Ms Melissa Middleton and Ms Jiaxin Zhang.
Multiple imputation has become a de facto standard for handling missing data in epidemiological and clinical research. With a combination of lectures and computer practicals (Stata and R), this workshop will cover introductory and advanced topics in multiple imputation that are critical in modern research studies.
Week Two (22-25 February): Modern Concepts in Clinical Trials: Adaptive Designs and the Estimand Framework
- An introduction to Adaptive Trial Designs (22nd February)
- Practical approaches to Adaptive Trial simulation (23rd and 24th February)
- Refining your research question: The Estimand framework (25th February)
Presented by: Prof Katherine Lee (Convenor), Dr Julie Marsh, Dr Kaushala Jayawardana, Prof Leonid Churilov, Dr Robert Mahar, Mr Michael Dymock and Ms Sabine Braat.
Trialists are increasingly turning to designs that can adapt to internal evidence or emerging external factors as the study progresses. This series of workshops provides a comprehensive manual of "How to implement an adaptive trial"; initially using lectures for a nontechnical overview and later computer practicals in both R and Stata to design and implement a simple parallel group design. There will also be a half day workshop introducing the estimand framework (ICH E9 (R1), 2019), which links the estimates of the treatment effect to the trial's objectives, accounting for events that may occur during the trial.