Ruth Keogh

The Risks of Risk Assessment: Causal Blind Spots When Using Prediction Models for Treatment Decisions | Annals of Internal Medicine Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already receiv...

New paper in @annalsofim.bsky.social

"50 ways to misinterpret clinical prediction models for treatment decisions”

--> Published version: www.acpjournals.org/doi/10.7326/...

--> Open access version: arxiv.org/pdf/2402.17366

Congratulations Georgia! It's a really nice paper.

🚨 Next month, we’ll be hosting a one-day event on target-trial emulation and other frameworks, exploring the role and potential of observational data for evaluating the effects of interventions

Open to everyone working in #datascience #biostatistics #clinicaltrials

Get your ticket 🔽
bit.ly/TTE_25

PhD Candidates Causal machine learning – Performance assessment of causal predictive algorithms | LUMC Do you want to work on challenging problems within causal inference and contribute to algorithms that support treatment decisions for individual patients? As PhD candidate causal machine learning at t...

HIRING!

2 PhD openings within the “Safe Causal Inference” consortium with experts from biostatistics, computer science, math, and epidemiology.

You'll develop new methods to evaluate prediction algorithms that take the causal effect of treatments into account.

👉 www.lumc.nl/en/about-lum....

Target trial emulation and other frameworks | LSHTM Large scale observational data are increasingly available for healthcare research, including from electronic health records and disease registries. Target trial emulation (TTE) provides a framework

🎫Registration open for 1-day hybrid event: “Target trial emulation and other frameworks: The role and potential of observational data for evaluating effects of interventions”. 📆26th June. tinyurl.com/dash-tte. Hosted by LSHTM Centre for Data & Statistical Science for Health. @lshtm-dash.bsky.social

Similarly a case-cohort study is regarded as a type of case-control sample, and the analysis is typically based on Cox regression and pseudo partial likelihood. Both analyses (nested cc and case-cohort) result in HR estimates.

Agreeing with @pausalz.bsky.social. A nested case-control study uses cases and selects controls from the risk sets. The analysis is typically based on a (stratified) Cox model and partial likelihood.

This hybrid event is aimed at people interested in real-world evidence, causal inference and trial emulation in health research - including statisticians, epidemiologists, data scientists, clinicians, decision-makers,.... 🎫Information on how to register to follow soon! 🎫. @lshtm-dash.bsky.social

📆 SAVE THE DATE: 26 June 📆 for our 1-day event on “Target trial emulation and other frameworks: The role and potential of observational data for evaluating effects of interventions”, hosted by the Centre for Data & Statistical Science for Health (DASH) at LSHTM. @lshtm-dash.bsky.social

👋 Hi Bluesky. We’re the Centre for Data and Statistical Science for Health (DASH) at @lshtm.bsky.social

We are the hub for data and statistical science in LSHTM, applying our expertise and data science resources to the biggest problems in global health

Find out more about us 👇

Demystifying Bayesian meta-analysis for researchers | LSHTM Bayesian models offer a powerful framework for meta-analysis through their flexible and probabilistic treatment of uncertainty.There are several methodological challenges in evidence synthesis,

3rd April, in London and online, come and hear @rlgrant.bsky.social talk about his new book with Gian Luca Di Tanna on Bayesian meta-analysis. Further details at www.lshtm.ac.uk/newsevents/e...

www.lshtm.ac.uk/newsevents/e...

seminar announcement

Excited to speak in the CAUSALab Methods Series tomorrow

Four year postdoc position at the Oslo Centre for Biostatistics and Epidemiology (OCBE) (272144) | University of Oslo Job title: Four year postdoc position at the Oslo Centre for Biostatistics and Epidemiology (OCBE) (272144), Employer: University of Oslo, Deadline: Friday, January 31, 2025

We're still seeking candidates interested in causal inference and/or survival analysis for a postdoc position in Oslo. Deadline January 31.

Please apply or forward this to anyone who might be interested!

www.jobbnorge.no/en/available...

Seminar series on ‘Causal Inference in Environmental Epidemiology’ hosted by the DASH Centre, with experts from Harvard, North Carolina State, and LSHTM.
First seminar on 22 January (15:00 - 16:00).
Info and links:
www.lshtm.ac.uk/newsevents/e...

#CausalInference #LSHTM #AirPollution #DataScience

Postdoc Biomedical Data Scientist / Biostatistician | LUMC In this postdoc position at LUMC, you will work on groundbreaking research that enhances the transparency and trustworthiness of decision support algorithms in healthcare. This position allows you to ...

Vacancy for a postdoc position.

Improve the transparency of decision support algorithms by figuring out how we can quantify and communicate uncertainty in individual causal predictions.

With Marleen Kunneman, Daniala Weir and me.
Three more days to apply 👇

www.lumc.nl/en/about-lum...

📣 Do you want to learn about recent advances in causal inference?

Colleagues at INSERM are organising a workshop gathering international experts in the field. Bonus: it's happening in two amazing locations 🌇🇫🇷

Really enjoyed this session on "Simulation studies for good practice in biostatistics", which was really well-attended and lots of interesting discussion.

🔥Air pollution from landscape fires associated with over 1.5 million deaths globally per year.

New research in The Lancet co-authored by Malcolm Mistry
& Antonio Gasparrini from #EHMLab at LSHTM assessed the global impact of #AirPollution from landscape fires.

#PublicHealth

👉 bit.ly/3R3LiZY

Hmm, I have an outfit like this too! Glad to be wearing the correct uniform. Looks like a fun trip!

Simulating Data From Marginal Structural Models for a Survival Time Outcome Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be...

New paper from Shaun Seaman and me on how to simulate data from marginal structural models (MSMs) for survival outcomes, including Cox MSMs. This can be useful in simulation studies evaluating causal inference methods that use MSMs. R code provided. onlinelibrary.wiley.com/doi/10.1002/...