Bianca De Stavola, Michalis Katsoulis, and Andrea Aparicio Castro
This introductory course is for anyone wishing to understand how comparisons of the effectiveness of alternative therapies or interventions can be performed using real world data (RWD) when adopting the framework of target trial emulation (TTE).
RWD are data on the everyday experiences of individuals that are collected through surveys, cohort studies, administrative and clinical. These data are observational, as opposed to experimental. Because of this, using them to address causal questions such as those of comparative effectiveness raises many concerns and difficulties. In this course we will describe the main sources of bias affecting RWD, describe how TTE can address some of them, and discuss its application in group discussions and computer practicals (in Stata and R).
To develop an understanding of the main challenges arising from using RWD in comparative effectiveness research and how to implement TTE to address at least some of them.
The course will consist of live lectures and practicals over three days. Participants are expected to be familiar with directed causal diagrams and regression models.
Monday 9th October
Live session: Lecture 1
Why target trial emulation?
Live session: Lecture 2
Target trial emulation in practice
Practical 1: Group discussion
Discussion of Practical 1
Tuesday 10th October
Live session: Lecture 3
Estimands of interest and their estimation
Computer practical 2 (Stata and R): estimating the observational-analogues of the ITT and PP effects for continuous end-of-study outcomes
Discussion of Practical 2
Wednesday 11th October
Live session: Lecture 4
Estimands for time-to-event outcomes
Computer practical 3 (Stata and R): estimating the observational-analogues of the ITT and PP effects for time-to-event outcomes
Discussion of Practical 3
Overview of the course and Discussion
It is advisable that, before attending this course, participants attend both the module called “Addressing Causal Questions using real work data: an Introduction” and the short course called “Introduction to Causal Diagrams”, or equivalently be familiar with the first 2 parts of the book by Hernán and Robins “ Causal Inference: What If.” which is available here:
This is a UKRI funded project offering rigorous training in longitudinal data science. Please note that this training is NOT available to undergraduate or masters students.