Arthur Chatton

Folks, always share your code. It doesn’t have to be perfect to be helpful. And if you feel that it’s still too messy or not sufficiently clean to be shared, you shouldn’t submit yet. After all, there could be mistakes in your mess.

Assistant or Associate Professor in Epidemiology
Montreal, Quebec, Canada United States #Epijobs
careers.apha.org/jobs/2125722...

Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn Trang Quynh Nguyen, Ian Schmid, Elizabeth A. Stuart Johns Hopkins Bloomberg School of Public Health The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements – effect definitions with causal interpretation, clarification of assumptions required for e ect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this paper is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these e ects with different types of research questions, and using concrete examples for illustration. This presentation differentiates two perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total e ect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the paper proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types – interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect...

Just discovered this excellent paper on mediation analysis in Psych Methods. The focus defining various effects; I really appreciate how the authors contrast the "traditional" approach with "causal" mediation analysis. Great job picking up readers where they are!
www.researchgate.net/publication/...

Comparison of open-source software for producing directed acyclic graphs Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to...

Potentially useful paper: doi.org/10.1515/jci-...

Multiple Imputation for Longitudinal Data: A Tutorial Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thu....

Hot off the press! 📣📣In this tutorial we illustrate available multiple imputation approaches for handling longitudinal data including when they are clustered within higher level clusters. A reproducible example with R and Stata code provided! #OpenAccess

onlinelibrary.wiley.com/doi/10.1002/...

New research alert! Our study investigates the effectiveness of human-only, AI-assisted, and AI-led teams in assessing the reproducibility of quantitative social science research. We've got some surprising findings!

A strong contender, at least: arxiv.org/abs/2405.08675

📣 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 🌇🇫🇷

NEW PREPRINT

A detailed overview of 32 popular predictive performance metrics for prediction models

arxiv.org/abs/2412.10288

Schrodinger's cat, but the radioactive source is labeled "reviewers' comments", the hammer for the poison is labeled "editor's decision", and the alive cat is labeled "beta-hat is machine learning" and the dead cat is labeled "beta-hat is not machine learning"

best I got for Schrodinger's regression

Leaky Clinical Prediction Models

The "Leaky prognostic model adoption pipeline" by @maartenvsmeden.bsky.social and colleagues is probably one of my most used figures when discussing building useful clinical prediction models. See the full paper here: publications.ersnet.org/content/erj/... #MLSky #stats #rstats #statistics

Was recently reminded of David Hand's alternative missing data taxonomy renaming the (in)famous taxonomy MCAR/MAR/MNAR by Donald Rubin to NDD/SDD/UDD. I am not generally a fan of renaming things, but this might be the exception

Source: rss.org.uk/training-eve...

A conversation on treatment effects The trial statistician and the clinical investigator took a step back to admire their creation.

You can't understand what tx effects can be estimated using clinical RCTs without understanding the REAL-WORLD context that clinical RCTs are conducted in. How patients are enrolled, and how medicines are "approved" are critical parts of this context.

(ICYMI)

statsepi.substack.com/p/a-conversa...

Since I have new followers, time to re-up this:

do you want to use my textbook (EPIDEMIOLOGY BY DESIGN) to teach? I have materials to share! I will give you lecture notes and exercises and exams and more!!

Open Case Studies: Statistics and Data Science Education through Real-World Applications With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice statistical thinking, as defin...

There are so many people out there trying to "fix" how we teach statistics and statistical thinking. Here is just one of many many examples. Empower them! Sure, it costs money to revamp curricula, but we do it all the time in medicine. Why not for stats!?

www.tandfonline.com/doi/full/10....

Hidden Imputations and the Kaplan-Meier Estimator The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized b...

If you're looking for a spoOoOoOoky epidemiology paper for Halloween, might I recommend this one

TLDR: the Kaplan-Meier estimator (with late entries) is haunted

www.ncbi.nlm.nih.gov/pmc/articles...

Every so often I'm reminded that a few of my tweets were included in a scientific paper and I'm still not exactly sure how I feel about that.

trialsjournal.biomedcentral.com/articles/10....

Ellie sitting at a table coloring at the library, with bookshelves as far as the eye can see.

Words cannot describe how wonderful libraries are. They are true treasure of society. The fact that they are getting their funding cut so police forces can have tanks and tactical gear is a true crime against culture.

Libraries are one of the greatest things in earth, no hyperbole.

Do grant proposal texts matter for funding decisions? A field experiment - Scientometrics Scientists and funding agencies invest considerable resources in writing and evaluating grant proposals. But do grant proposal texts noticeably change panel decisions in single blind review? We report...

"A random half of panelists were shown a CV and only a one-paragraph summary of the proposed research, while the other half were shown a CV and a full proposal. We find that withholding proposal texts from panelists did not detectibly impact their proposal rankings"
link.springer.com/article/10.1...

Is [insert statistical approach] good or bad? Let’s settle the debate, once and for all I don’t like getting into fights and sometimes I am concerned this keeps me from becoming a proper methods/stats person. Getting into fights about one or multiple (or all) of the following just seems ...

Great post here. Touches on so many interesting points about causal inference, estimands, development of methods, operator skill, etc. I encourage people who work on methods to read it!
@dingdingpeng.the100.ci
www.the100.ci/2024/04/13/i...

Causal inference for psychologists who think that causal inference is not for them. Correlation does not imply causation and psychologists' causal inference training often focuses on the conclusion that therefore experiments are needed—without much consideration for the causal inference frameworks used elsewhere. This leaves researchers ill-equipped to solve inferential problems that they encounter in their work, leading to mistaken conclusions and incoherent statistical analyses. For a more systematic approach to causal inference, this article provides brief introductions to the potential outcomes framework—the “lingua franca” of causal inference—and to directed acyclic graphs, a graphical notation that makes it easier to systematically reason about complex causal situations. I then discuss two issues that may be of interest to researchers in social and personality psychology who think that formalized causal inference is of little relevance to their work. First, posttreatment bias:...

DAG illustrating posttreatment bias which can be induced in randomized experiments whenever researchers condition on posttreatment variables

Figure illustrating various reasons why demonstrations of incremental validity may be unimpressive: established predictors are omitted, measurement error is ignored, only little predictive utility is gained

Do you think that learning more about causal inference is not worth it because you're running experiments anyway, or because you're interested in predictive questions? In that case, I've written a paper just for you, out now in SPPC: compass.onlinelibrary.wiley.com/doi/10.1111/...

Periodic reminder, episky medsky! If you teach epidemiology and might be interested in using my textbook (EPIDEMIOLOGY BY DESIGN) --

I will send you ALL MY TEACHING MATERIALS (lecture slides; practice problems; exercises; exams + keys; sample syllabi...)

Just ask! And also --

“Neutral Comparison Studies in Methodological Research”

Our special collection appeared in Biometrical Journal is now complete! Thanks to all authors and reviewers!

onlinelibrary.wiley.com/doi/toc/10.1...

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Thank god for @khoavuumn.bsky.social

First substack post of the year!
It's on simulation studies and reviews of methodology.
tpmorris.substack.com/p/simulation...

Personal reflection: "Clinical prediction models & the multiverse of madness"

Thanks to BMC Medicine for 'getting this'

Many reviewers/Eds pushed for writing style & tone changes

This thread delves into this & why we stuck to our original vision

bmcmedicine.biomedcentral.com/articles/10....

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 We argue that there is a replication crisis in methodological research, see this magazine style paper for an overview:
rss.onlinelibrary.wiley.com/doi/epdf/10....
2/4

I'm not saying you can't possibly generate a worthwhile hypothesis from your data. I'm just saying that generating a hypothesis from the entirety of human knowledge that preceded your data is a much safer bet.

Here is a 1-page summary for your wall

This talk is based on our Christmas article from 2022

t.co/y4GVFmTOfs

🎉 Thrilled to share that our manuscript on natural experiments has just been accepted at AMPPS!
with @dingdingpeng.the100.ci Adam Ayaita @ruben.the100.ci @p-hunermund.com @azwpsy.bsky.social Susanne Bücker, Sven Rieger, Sandrine Müller, and Tobias Ebert! osf.io/preprints/ps...