Postdoctoral Research Fellow in Statistics | PhD | MRC-NIHR awardee in Causal Inference methods @LSHTM (@icon_lshtm)
Joined November 2019
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🚨New paper🚨 Tutorial on causal inference in Stata, R, and Python bit.ly/3pMAKAK
Code on GitHub! bit.ly/3nE3cCg
with @Mohamma70696197 @WATZILEI @camaringe @PausalZ @nondogmatist @LeyClem @aurelien_belot @icon_lshtm
@epi_twit @statstwitbot @CausalInferBot
📢 New paper in Statistics in Medicine!
We compare TMLE vs CV-TMLE across tough simulation settings:
✔️ Misspecification
✔️ Limited overlap
✔️ High-dimensional data
✔️ Super Learner + cross-fitting
Read it here: onlinelibrary.wiley.com/doi/…
@epi_twit @statstwitbot
Matthew J. Smith retweeted
🚨 New #SystematicReview in @AnnalsOfEpi with @rachaelvp, @watzilei, @camaringe 🎉
How has Targeted Maximum Likelihood Estimation (#TMLE) been applied in public health and epidemiological studies?👉 bit.ly/3NmbVWM
TL;DR #Thread 🧵
(@UKRI_News @The_MRC @NIHRresearch)
🚨 New #SystematicReview in @AnnalsOfEpi with @rachaelvp, @watzilei, @camaringe 🎉
How has Targeted Maximum Likelihood Estimation (#TMLE) been applied in public health and epidemiological studies?👉 bit.ly/3NmbVWM
TL;DR #Thread 🧵
(@UKRI_News @The_MRC @NIHRresearch)
#TMLE has been methodologically adapted to multiple data settings:
1. Small sample size
2. (Nested) Case-control studies
3. Collaborative TMLE
4. Longitudinal data
5. Sequential randomised trials
6. Mediation analysis
7. Meta-analysis
8. Cross-validated TMLE
9. One-step TMLE
etc
ALT Applied clinical and epidemiological research by year of publication and TMLE method implemented.
We argue that dissemination of #TMLE relies on 5 key factors:
1. Software availability (doi.org/10.1002/sim.9234) 🧑💻
2. Accessibility of available material 📄🔓
3. Number of experts in #TMLE 🤓🤓🤓
4. Teaching 🏫🧑🎓
5. Collaborations 🧑🎓🤝🧑💼🤝🧑💻🤝🧑🔬🤝🧑⚕️ ...... 💡...... 🤯🤯🤯
Matthew J. Smith retweeted
New #SystematicReview available in @AnnalsOfEpi - by @MJTSmith, @rachaelvp, @WATZILEI & @camaringe 👏
The team investigated the application of #TMLE in public health and epidemiological studies👇
sciencedirect.com/science/ar…
Matthew J. Smith retweeted
G-formula for causal inference via multiple imputation thestatsgeek.com/2023/01/31/…
Matthew J. Smith retweeted
📢 New publication by @icon_lshtm researchers highlights importance of diagnostic route in survival of people with #lymphoma and comorbidities.
➡️mdpi.com/1889940
Further research is vital to ensure timely diagnoses to reduce the #inequalities in #cancer survival.
Matthew J. Smith retweeted
Save the dates! EuroCIM 2023 will take place in Oslo, April 19-21 (with short courses April 18). Keep an eye on eurocim.org for more information.
Matthew J. Smith retweeted
Coming soon...
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🚨 Another causal inference tutorial 🚨
Check out our new tutorial on the Delta-Method and Influence Function for estimating the variance of an estimand in causal inference 👇
@CausalInferBot @statstwitbot @epi_twit @WATZILEI
A common way to build confidence intervals is the #DeltaMethod. It generates CI of numbers, vectors, and functions.
We've made a tutorial showing how the functional delta method works and its connection with the influence function in #epi #tutorial
🧵
arxiv.org/abs/2206.15310
Matthew J. Smith retweeted
You want to know more about the connection between the #influencefunction and the #Deltamethod ? Check out our #preprint we made with @camaringe @MJTSmith @aurelien_belot @mf_schomaker
@icon_lshtm @mirschnitzer @watzilei
All comments are welcome!
Matthew J. Smith retweeted
Living with or caring for someone with #cancer can feel overwhelming. But connecting with others to share experiences and tell our stories can help to find meaning and purpose, to understand others and ourselves. Join us in July to listen and be heard: bit.ly/CancerAndMe
Matthew J. Smith retweeted
Slides from a talk on 'Hypothetical estimands in clinical trials – a unification of causal inference and missing data methods' at the Danish Society for Biopharmaceutical Statistics #estimands #hypothetical thestatsgeek.com/wp-content/…
Matthew J. Smith retweeted
We're excited to announce that next up in our @LSHTM_CGCC & @icon_lshtm seminar series on inequalities in cancer outcomes is @epi_nina - presenting her work on socio-economic inequalities in survival from colon cancer:
🗓️Weds 18th May @ 12:45 (GMT+)
icon.lshtm.ac.uk/events/asse…
Matthew J. Smith retweeted
Come and join us! We have a new research fellow position available in our warm and friendly team @UniofYork. If you are interested in #epidemiology and #statistics or #cancer we would love to hear from you
bit.ly/3vIZGMi
#haematology
Matthew J. Smith retweeted
If you’ve considered using Targeted Maximum Likelihood Estimation in the past for an observational study and decided not to use it, what was the main reason for not using it?
@epi_twit @statstwitbot @CausalInferBot #epitwitter #statstwitter #causalinference #TMLE
59%Didn’t understand TMLE
29%Needed extension of TMLE
0%No suitable reviewer
12%Other (please comment)
17 votes • Final results