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Lachlan Cribb

@lachcribb.bsky.social

PhD student working on causal inference methods & dementia at Monash University. lachlancribb.netlify.app

36 Followers  |  146 Following  |  5 Posts  |  Joined: 26.10.2024  |  1.8201

Latest posts by lachcribb.bsky.social on Bluesky

Science people!!

What software are you using to draft papers (besides MS Word ๐Ÿคฎ)? Looking for something that
1. Allows for easy collab (with non-tech ppl, so latex is out)
2. In format accepted by journals (so no typst?)
3. Has reference manager integration

Is it just googledocs/libreoffice?

13.08.2025 03:36 โ€” ๐Ÿ‘ 1    ๐Ÿ” 0    ๐Ÿ’ฌ 0    ๐Ÿ“Œ 0

Just to note, shared folders on google drive etc (anything that syncs changes) can be a real nightmare if you do end up collaborating via git & github

24.04.2025 12:03 โ€” ๐Ÿ‘ 2    ๐Ÿ” 0    ๐Ÿ’ฌ 1    ๐Ÿ“Œ 0
Guide to SuperLearner

Frequentist alternative to Bayesian model averaging: Incorporate all 6 specifications into a superlearner stack, letting the data dictate how much weight each model receives in the ensemble. (Might need a doubly robust estimator/bootstrapping for valid inference). cran.r-project.org/web/packages...

09.03.2025 07:00 โ€” ๐Ÿ‘ 9    ๐Ÿ” 1    ๐Ÿ’ฌ 0    ๐Ÿ“Œ 0

That's great! With MSM's, is it straightforward to incorporate the estimation of the weights and the weighting of the regression model (Y on A*) with those estimated weights?

04.03.2025 00:32 โ€” ๐Ÿ‘ 2    ๐Ÿ” 0    ๐Ÿ’ฌ 1    ๐Ÿ“Œ 0
Preview
Moving beyond the prevalent exposure design for causal inference in dementia research As randomised trials are not always feasible or practical, observational studies remain crucial for addressing many causal questions in the dementia prevention field. Through a systematic search, we f...

New paper!! We find that prevalent exposure designs are very common in dementia research, explain why they are ill-suited for causal inference, and show how the target trial framework helps us move beyond them.

Read here๐Ÿ‘‡ www.thelancet.com/journals/lan...

02.02.2025 23:51 โ€” ๐Ÿ‘ 5    ๐Ÿ” 3    ๐Ÿ’ฌ 0    ๐Ÿ“Œ 0

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