10th December 2022

Introducing Lewis:

Hi I’m Lewis. I will be leading on the analysis for the project, with technical support from Paul and Noemi. I will also be working with Lauren to help incorporate lived-experience into the analyses.

I am a Lecturer in Data Science at the University of York. My background is in Bayesian Statistics, and I got a PhD in this area at Durham University. I have a general interest in a wide range of quantitative methods, including machine learning and targeted learning! My post-doctoral work mainly focussed on applying these techniques to medical selection and regulation, but more recent work has focussed on mental health.

Outside of work, I am a keen golfer and supporter of Darlington FC, although both activities can at times be more frustrating than enjoyable!


Comments

Post a Comment

Popular posts from this blog

Co-producing reporting guidelines for targeted learning studies

24th February 2023 There has been well publicised problems with reproducible and transparent reporting from observational data based research studies. This is a particular issue once machine learning gets involved, come due to the complexity. Many of the studies that are published would be difficult to reproduce, even by the research team themselves. Therefore today we had a workshop with two lived experience expert researchers, an additional lived experience expert and myself. Together we went through the draft reporting guidance we have been creating for targeted learning studies. This was a very useful exercise and helped harness lay researcher and lived experience into the guidance. This was important not just for making plans of analysis and protocols available publicly before analysis starts. It was also important in thinking about how results are reported and disseminated and the role of lived experience experts in this process. These guidelines were intended to build on s
Read more
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
Read more

The 'Table 2 fallacy'

 21st December 2022 I was asked a question the other day that got me reflecting on some issues in causal inference. The question was this: ‘…if a multivariable model includes all the relevant potential confounders then what is the advantage of using causal modelling methods ?’ Having reflected a bit on my own training in causal inference and after a quick Google I found this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626058/ The ‘Table 2 Fallacy’ refers to the assumption that all the effects of covariates estimated in a multivariable model are independent effect sizes. Indeed, the main exposure of interest is treated like any other covariate within a multivariable model.  It’s called the ‘Table 2 Fallacy’ because observational studies often use Table 1 to present the descriptives of the data used and Table 2 the results of a multivariable analysis. In my case it should probably be called the ‘Table 3 Fallacy’ as I usually use Table 2 to present my univariable results.  Interp
Read more