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Jonathan Bartlett @jonathan-bartlett.bsky.social · Sep 3

Thinking of performing a quantitative bias analysis for measurement error or misclassification? Then our recent software review, by Codie Wood, Kate Tilling, myself and Rachael Hughes, may be of interest: rdcu.be/eDRn2

Quantitative bias analysis for mismeasured variables in health research: a review of software tools | BMC Medical Research Methodology

rdcu.be

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Reposted by Jonathan Bartlett

Suzie Cro @suziecro.bsky.social · Sep 1

Are estimands being correctly used?
A new review of protocols led by Timothy Clark shows many incorrectly defined estimand attributes. See the top areas for improvement & full results here:
trialsjournal.biomedcentral.com/articles/10.... #Trials

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Aug 26

I gave the same talk earlier in the year at the @causalab.bsky.social and this is online youtu.be/2E3NusvsMaI?...

2025 CAUSALab Methods Series with Jonathan Bartlett

YouTube video by CAUSALab at Harvard T.H. Chan

youtu.be

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Reposted by Jonathan Bartlett

Myra McGuinness @myramcguinness.bsky.social · Aug 11

September @vicbiostat.bsky.social seminar:

Camila Olarte Parra from @causalab.bsky.social Karolinska will speak on combining information from trial participants and non-participants in registry-based trials.

All welcome online 25 September.

More info:
www.vicbiostat.org.au/event/combin...

Combining information from trial participants and non-participants in registry-based trials

Even though the advantages of randomised tria

www.vicbiostat.org.au

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Aug 20

We are recruiting a Research Fellow to develop machine learning based methods for handling missing data @lshtm.bsky.social. See jobs.lshtm.ac.uk/vacancy.aspx... for more details.

Job Opportunity at LSHTM: Research Fellow

The London School of Hygiene & Tropical Medicine (LSHTM) is one of the world’s leading public health universities. Our mission is to improve health and health equity in the UK and worldwide; working i...

jobs.lshtm.ac.uk

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Reposted by Jonathan Bartlett

Myra McGuinness @myramcguinness.bsky.social · Jul 23

We are looking forward to hearing @jonathan-bartlett.bsky.social speak on the G-formula for causal inference using synthetic multiple imputation at the July @vicbiostat.bsky.social seminar!

All welcome online Thursday 24th, 4:00pm Aus EST (7:00am UK time).

www.vicbiostat.org.au/event/g-form...

G-formula for causal inference using synthetic multiple imputation

G-formula is a popular approach for estimatin

www.vicbiostat.org.au

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Reposted by Jonathan Bartlett

Nan van Geloven @gelovennan.bsky.social · May 19

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....

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...

www.lumc.nl

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Reposted by Jonathan Bartlett

Margarita Moreno-Betancur @margaritamb.bsky.social · Jul 10

1/ NEW R PACKAGE! For estimating the impact of potential interventions on multiple mediators in countering exposure effects (led by @cttc101.bsky.social)

- Paper👉 tinyurl.com/ye26jsps
- Package👉 tinyurl.com/yuh4kens

Thread shows published examples of how the method can be used! #EpiSky #CausalSky

tinyurl.com

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Reposted by Jonathan Bartlett

Data & Statistical Science for Health, LSHTM @lshtm-dash.bsky.social · Jun 10

📣 Calling everyone working in #datascience #biostatistics #clinicaltrials

We’re bringing together experts on target-trial emulation and other frameworks, where we’ll explore the role and potential of observational data for evaluating the effects of interventions

Don’t miss out 🔽
bit.ly/TTE_25

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Reposted by Jonathan Bartlett

Data & Statistical Science for Health, LSHTM @lshtm-dash.bsky.social · May 27

🚨 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

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Jonathan Bartlett @jonathan-bartlett.bsky.social · May 21

I probably misunderstand, but when you install a package it will install other packages it depends on. And then when you load the package with library() it loads the dependencies likewise.

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Jonathan Bartlett @jonathan-bartlett.bsky.social · May 13

Join us on 10th June (online or in London @lshtm-dash.bsky.social ) to hear from Matthew Sperrin talk about his work on 'Prediction under intervention: challenges and trade-offs'.More details at www.lshtm.ac.uk/newsevents/e...

Prediction under intervention: challenges and trade-offs | LSHTM

Causality and prediction are often two separate activities. In particular, prediction can be done in a way that is agnostic to underlying knowledge, mechanism or causal structure. However, it is very

www.lshtm.ac.uk

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Reposted by Jonathan Bartlett

Ruth Keogh @ruthkeogh.bsky.social · Apr 24

📆 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

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Apr 3

Indeed. This paper is a good overview of the ICH E9 addendum on estimands on this topic: doi.org/10.1136/bmj-...

The estimands framework: a primer on the ICH E9(R1) addendum

Estimands can be used in studies of healthcare interventions to clarify the interpretation of treatment effects. The addendum to the ICH E9 harmonised guideline on statistical principles for clinical ...

doi.org

Jonathan Bartlett @jonathan-bartlett.bsky.social · Apr 3

Yes it could. These hypothetical estimands do indeed deviate from what I have always interpreted ITT to mean. For me ITT means analyse according to randomised group and look at outcomes irrespective of events such as treatment switch.

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Apr 3

Should data observed after intercurrent events handled by the hypothetical strategy be used in estimation of treatment effects? Rhian Daniel and I investigate... thestatsgeek.com/2025/04/03/t...

The role of post intercurrent event data in the estimation of hypothetical estimands in clinical trials

Clinical trial estimands which make use of the so-called hypothetical strategy target the effect of one randomised treatment compared to another in a scenario where the corresponding intercurrent e…

thestatsgeek.com

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Apr 2

'G-formula with multiple imputation for causal inference with incomplete data'. Open access in Statistical Methods in Medical Research. doi.org/10.1177/0962...

Sage Journals: Discover world-class research

Subscription and open access journals from Sage, the world's leading independent academic publisher.

doi.org

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Reposted by Jonathan Bartlett

Ghazaleh Dashti @ghazalehd.bsky.social · Apr 2

📣 📣NEW PAPER providing guidance on best practice for using multiple imputation when estimating interventional mediation effects, considering missingness mechanism, multiple imputation model specification, & variance estimation
#CausalSky #EpiSky

Read more 👇🏽
journals.lww.com/epidem/abstr...

Handling multivariable missing data in causal mediation... : Epidemiology

miologic studies. However, guidance is lacking on best practice for using multiple imputation when estimating interventional mediation effects, specifically regarding the role of missingness mechanism...

journals.lww.com

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Reposted by Jonathan Bartlett

Alejandro Schuler @aschuler.bsky.social · Mar 31

New paper! We extend my prior work on prognostic adjustment to work with generalized linear models. This is a nice way to gain power in randomized trials (eg with binary outcomes) by leveraging historical data in a way that does not sacrifice type I error control.

arxiv.org/abs/2503.22284

Powering RCTs for marginal effects with GLMs using prognostic score adjustment

In randomized clinical trials (RCTs), the accurate estimation of marginal treatment effects is crucial for determining the efficacy of interventions. Enhancing the statistical power of these analyses ...

arxiv.org

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Mar 28

Sorry. I agree with you! My initial reaction/thinking was that in conditional imputation there are two variables in play, with one only defined in those for whom the first takes a certain value. But as you indicate, you can translate this into a problem with one variable. Thank you!

Jonathan Bartlett @jonathan-bartlett.bsky.social · Mar 27

Probably looking at the example in the vignette will (hopefully!) make it clear.

Jonathan Bartlett @jonathan-bartlett.bsky.social · Mar 27

Not the same I don't think. This is about a situation similar to censoring- you have partial info about the missing values. The smcfcs additions are though for factor variables, where instead of the exact category, you know someone belongs to one among a subset of the categories...

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Mar 27

Imputation of factor variables when you have partial information about some of the missing values. See here for more details thestatsgeek.com/2025/03/27/m...

Multiple imputation for coarsened (grouped) factor covariates

Missing data are a common problem in statistical analyses. A closely related but slightly different problem is when for an individual in a dataset, although we do not know the exact value of a part…

thestatsgeek.com

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Jonathan Bartlett @jonathan-bartlett.bsky.social · Mar 19

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...

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,

www.lshtm.ac.uk

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Reposted by Jonathan Bartlett

The EuroCIM @eurocim.bsky.social · Mar 11

The EuroCIM program is live! Explore the sessions, speakers, and schedule here: www.eurocim.org/program.html. Get ready for an exciting conference! 🎉

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