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Reposted by Didier Brassard

Ian Hussey @ianhussey.mmmdata.io · 4d

My article "Data is not available upon request" was published in Meta-Psychology. Very happy to see this out!
open.lnu.se/index.php/me...

LnuOpen | Meta-Psychology

open.lnu.se

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Reposted by Didier Brassard

Chelsea Parlett @chelseaparlett.bsky.social · 5d

It’s not the method that makes you causal it’s the assumptions

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Reposted by Didier Brassard

Danielle Navarro @djnavarro.net · 11d

A few words on survey weights, why I'm embarrassed to have forgotten to take them into account in the past, and how I got lucky because I personally didn't get burned. Not a mistake I intend to repeat in the future

blog.djnavarro.net/posts/2025-0...

Some notes on survey weights – Notes from a data witch

An area of statistics in which the author is not strong, and really needs to up her game

blog.djnavarro.net

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Reposted by Didier Brassard

Peter Tennant @pwgtennant.bsky.social · Sep 3

The TARGET reporting guidelines for target trial emulation studies have arrived!

#EpiSky #CausalSky

jamanetwork.com/journals/jam...

TARGET 2025 Statement

This Special Communication introduces the Transparent Reporting of Observational Studies Emulating a Target Trial (TARGET) 2025 guideline, a consensus-based guidance for reporting observational studie...

jamanetwork.com

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Didier Brassard @didierbrassard.bsky.social · Sep 3

Why ruin my career like that before it even starts?!?

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Ryan Briggs @ryancbriggs.net · Sep 2

It's very human to only double check that a process is working when you get a weird result. It's also very bad practice, because sometimes your "right" result is due to a bad process and you will be misled. Social scientists (economists) do this kind of asymmetric checking.
arxiv.org/pdf/2508.20069

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Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities

Abstract
Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals).

Illustrated are
1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals
2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and
3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

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Reposted by Didier Brassard

Peter Tennant @pwgtennant.bsky.social · Aug 12

I'm reviewing a lot of weak target trial emulation studies these days.

Like a wolf in sheep's clothes, these adopt the language & structure of target trial emulation, but don't apply the necessary care or thought.

Is this the 'doom cycle'? Where every promising new tool gets dragged into the mud.

Darren Dahly @statsepi.bsky.social · Aug 12

Target trial emulation is an important, useful idea, but we must remain careful to not allow novices to think it's a magical shortcut to causal inference. Nor can we lose sight of the *main* thing that separates randomized from non-randomized studies.

POTENTIAL RISKS OF TARGET TRIAL
TERMINOLOGY
There are risks that the term target trial emulation (TTE)
might mislead readers without methodological training. We
believe some readers may assume TTE represents a distinct
form of observational study design, rather than a framework
to help researchers identify and avoid self-inflicted errors in
observational studies. We are particularly concerned that
claims to have emulated an RCT will be mistaken for a claim
to have successfully emulated an RCT. The latter statement
implies that the inference is of a comparable standard to a
well-conducted RCT. To be certain, the TTE framework (2),
used correctly, will help investigators specify estimands,
and sidestep avoidable selection bias, but we are probably
more pessimistic than Schwarze et al. (1) about the ability
of ‘‘advanced methods’’ to address the threat of other
sources of bias.

However, the difference in how
randomization accounts for confounding vs. ‘‘advanced
methods’’ like covariate adjustment, weighting, etc. is a
difference in kind, not in degree. This is because
randomization is guaranteed to eliminate confounding for
some key estimands, and to do so in a manner that
predominantly rests on our trust in a potentially verifiable
randomization process. In contrast, acting on the effect
estimate returned from an NRSI requires us to trust in the decisions and competence of the investigator, and even then,
no guarantee is possible, even in relatively simple sets of
relationships among a small set of potential covariates.
This distinction will be obvious to methodological experts,
but they only constitute a small proportion of researchers
and research-consumers

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Reposted by Didier Brassard

Emily Riederer @emilyriederer.bsky.social · Jul 27

Technical writing is hard bcs "writing is thinking" but we often should tell our story not in the order we worked. Solution? I wrote a quick post on how @quarto.org 's embed shortcodes can reframe technical writing as reproducible evidence curation

www.emilyriederer.com/post/quarto-...

🧵 (1/n)

How Quarto embed fixes data science storytelling | Emily Riederer

Literate programming excels at capturing our stream of conscience. Our stream of conscience does not excel at explaining the impact of our work. Notebooks enable some of data scientists’ worst tendenc...

www.emilyriederer.com

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Reposted by Didier Brassard

Kevin C Klatt, PhD, RD @kcklatt.bsky.social · Jul 25

It's exceedingly hard to argue that the administration & MAHA are committed to improving nutrition when they're simultaneously cutting everything from SNAP-Ed to innovative community nutrition work - cements the perception that food dyes are public health theatre.
www.healthbeat.org/newyork/2025...

Q&A: Nutrition expert discusses pioneering program terminated by USDA

Here’s a Q&A with a nutrition expert who created an after-school program for NYC middle-schoolers who take on adult responsibilities, like meal preparation. This spring, the USDA terminated it.

www.healthbeat.org

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Reposted by Didier Brassard

Ole Goltermann @olegolt.bsky.social · Jul 22

Serious concerns about a new cortical biomarker for pain sensitivity

jamanetwork.com/journals/jam...

We (with @tspisak.bsky.social, @christianbuchel.bsky.social) published a commentary on Chowdhury, Bi et al. (2025, JAMA Neurology) raising serious concerns about their reported results.

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Concern About Predictive Performance of a Pain Sensitivity Biomarker

To the Editor Chowdhury et al1 evaluated a biomarker for pain sensitivity, combining peak alpha frequency and corticomotor excitability. The authors report outstanding performance (validation set area...

jamanetwork.com

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Reposted by Didier Brassard

The BMJ @bmj.com · Jul 21

This article provides an overview of the current state of handling continuous variables in healthcare research.

It discusses the potential limitations of assuming a linear relationship between independent and dependent variables
www.bmj.com/content/390/...

Linear predictor plot for three modelling approaches to analyse continuous variables in a case study of cerebrospinal fluid glucose and acute bacterial meningitis

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Reposted by Didier Brassard

Jordan Nafa @jordannafa.bsky.social · Jul 15

Statistics/Causal Inference folks, what are your favorite papers on why VIF is bad?

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Reposted by Didier Brassard

Julia M. Rohrer @dingdingpeng.the100.ci · Jul 16

At this point, I might as well --
Here's an infographic showing different ways to include age as a predictor. The top shows two extremes, just as a plain old numerical predictor (imposes linear trajectory) vs. categorical predictor (imposes nothing whatsoever). And then three solutions in between!

Infographic illustrating different ways to model age.
First panel shows two "extreme" cases; including age as a linear numerical predictor (df = 1) or including age as a categorical predictor (df = number of years of age minus 1).
Second panel shows an intermediate solution in which age is categorized into broader bins (df = number of categories minus 1, here 5 - 1 = 4).
Third panel shows an intermediate solution in which age is included with a polynomial (df = degrees of freedom of the polynomial, here 4).
Fourth panel shows an intermediate solution in which age is modeled with the help of splines (df = degrees of freedom of the splines, here 4).

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Didier Brassard @didierbrassard.bsky.social · Jul 16

Fantastic article about modeling of continuous variables! ⬇️
Shameless plug: I wrote a blog about restricted cubic spline applied to nutrition and health data: didierbrassard.github.io/posts/2023/0...

Reposted by Didier Brassard

Tom Yates @tomayates.bsky.social · Jul 15

There are going to be an awful lot of analyses that need redone when people finally accept that collider bias is a major issue in unweighted analyses of UKBB data

Reweighting UK Biobank corrects for pervasive selection bias due to volunteering

AbstractBackground. Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). Th

share.google

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Reposted by Didier Brassard

Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · Jul 9

How can we reform science? I have some ideas. But I am not sure you’ll like them, because they don’t promise much. elevanth.org/blog/2025/07...

Which Kind of Science Reform

What hope is there for science reform, if we can't agree on what to reform? Right now, principles are more important than practices.

elevanth.org

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Didier Brassard @didierbrassard.bsky.social · Jul 8

I attended two CAUSALab Summer Schools, and spent nearly 2 years working through the design and implementation. In the end, I am proud of having done exactly the work I said I would do in my application. I believe it is the first target trial emulation of adherence to Canada’s Food Guide.

Didier Brassard @didierbrassard.bsky.social · Jul 8

This project is especially meaningful to me. Back in 2021, I proposed it in my postdoc funding application. At the time, I actually had no idea how to actually implement a target trial emulation 😅. I knew that this was the right framework to improve nutritional epidemiology studies. #nutepi

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Didier Brassard @didierbrassard.bsky.social · Jul 8

💡 Key message:  Canada’s Food Guide recommendations support healthy aging, but optimal muscle strength and function likely require more than just following CFG: more protein foods, and physical activity.

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Didier Brassard @didierbrassard.bsky.social · Jul 8

📄 New preprint!  The main project of my postdoctoral work is now available (not peer-reviewed yet): Estimating the effect of adhering to #CanadaFoodGuide 2019 recommendations in older adults: a target trial emulation 
🔗 www.medrxiv.org/content/10.1...

Estimating the effect of adhering to Canada’s Food Guide 2019 recommendations in older adults: a target trial emulation

Background The 2019 Canada’s Food Guide (CFG) provides universal recommendations to individuals aged 2 years or older. The extent to which these recommendations positively influence key health outcome...

www.medrxiv.org

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Didier Brassard @didierbrassard.bsky.social · Jul 5

Thanks for the suggestion. I have not yet used pubpeer. Looking forward to trying it

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Didier Brassard @didierbrassard.bsky.social · Jul 5

I guess I had the intuition it would not be published, since I had only sent a detailed abstract and outline for the letter. So, there was no preprint, but I am thinking about writing it anyway. Unsure where it’d land because the prompt for the letter is really the article in the specific journal

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Didier Brassard @didierbrassard.bsky.social · Jul 5

I agree, the challenges to self-correcting science are real.
Tried to publish a “letter to the editor” which was rejected in the end. The editor mentioned the topic of the letter wouldn’t be of interest to readers! Wouldn’t readers also be interested to learn about flaws of a published study?

Ian Hussey @ianhussey.mmmdata.io · Jul 5

I am often told that public critique of published articles must also solve the issues found. I think this frequently enforced requirement hinders scientific self-correction.

Blog post:

mmmdata.io/posts/2025/0...

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Reposted by Didier Brassard

Mike Frank @mcxfrank.bsky.social · Jul 1

Experimentology is out today!!! A group of us wrote a free online textbook for experimental methods, available at experimentology.io - the idea was to integrate open science into all aspects of the experimental workflow from planning to design, analysis, and writing.

Experimentology cover: title and curves for distributions.

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