13 January 2023 Whole team event The whole team, including the lay researchers, met again to discuss a preliminary Directed Acyclic Graph (DAG). This is a diagram that describes how different variables are thought to be causally related to each. It was co-produced by the researchers and experts by experience. The DAG was intended to capture important variables that had been raised previously by the lay researchers and a young people’s advisory group. The meeting was very useful. In addition to discussing the DAG, the data scientist on the project ran through some preliminary results of the machine learning analysis to see if the findings resonated with the experts by experience. This led to some debate and the PPI lead is going to follow up with the lay researchers individually following the meeting. Finally, I delivered some training on the concept of machine learning in order to de-mystify this approach. All team members contributed during the meeting and it was great to hear a varie
Posts
Showing posts from January, 2023
- Get link
- Other Apps
6th January 2023 Happy New Year! Over the Christmas period I looked again at the mathematics behind targeted learning. What is the 'magic' 🐇🎩behind this technique? I was familiar with machine learning so the first part was clear to me. This is where propensity scores and predicted probabilities for the outcomes are generated, usually through ‘superlearning’. However, previously I have never been quite sure how the ‘updating’ worked. Thankfully I found this helpful video from Susan Gruber, which explains the maths behind the ‘updating’ stage in detail. https://youtu.be/8Q9dfW3oOi4 There is also a helpful paper that explains the targeted learning process in detail here . The key issue is that, once the machine learning predictions are derived then they can be further updated by essentially regressing the residuals on to the observed outcomes for a validation data set. This involves using the predictions as an ‘offset’ in a logistic regression. In this context an ‘