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Didier Brassard

@didierbrassard.bsky.social

Professor of nutrition. 💻 Nutritional epidemiology, aging, dietary assessment and causal inference (at least trying) 📍 Université du Québec à Trois-Rivières

22 Posts28 Follower 60 Folgt

Julia M. Rohrer @dingdingpeng.the100.ci · 21h ago

From the makers of “the confidence interval is simply all values that we cannot reject” www.the100.ci/2024/12/05/w...

There is an alternative fully-Frequentist interpretation of confidence intervals that more closely connects them to the idea of null-hypothesis significance testing. The 95% confidence interval contains all possible population parameters that, if they were our null hypothesis, would not be rejected with an alpha of .05 because of our data.[8] One could shorten this to “the confidence interval contains all parameter values that we cannot reject” or “the confidence interval contains all parameter values that are compatible with the data.” The appeal of this is that it’s more snappy than any statement about coverage, while not being wrong according to Frequentist logic. It also does feel like an explanation, although when you think about it, it assumes that you have understood null hypothesis significance testing, and null hypothesis significance testing is confusing in its own right. If you can just assume that people have understood this one complicated thing, can’t you just assume that they have also understood confidence intervals and don’t need any interpretation at all? Then again, if you are deeply confused about the whole matter, maybe this is an elegant solution that buries any confusion one level deeper, where it won’t upset people who really care about confidence intervals.

Julia M. Rohrer @dingdingpeng.the100.ci · 6d ago

The within-person associations aren’t necessarily causal because of time-varying confounding, which is something we discuss here: journals.sagepub.com/doi/full/10..... However, we didn’t discuss the scenario of interpreting the between-person associations causally, because that’s…an interesting move

These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis - Julia M. Rohrer, Kou Murayama, 2023

In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data a...

journals.sagepub.com

Per Engzell @pengzell.bsky.social · 2mo ago

Short story about statistical modeling without causal inference gone badly wrong. Just head on the news that preschoolers play 15 minutes less on rainy days. The reporting stressed that the research was "associational" and therefore couldn't tell us why. Huh? 1/

Didier Brassard @didierbrassard.bsky.social · 2mo ago

Great overview of LLM 🤝 coding

Frank Harrell@f2harrell.bsky.social · 2mo ago

New presentation on my experiences and thoughts about the roles for AI in #Statistics: fharrell.com/talk/ai #LLM #ai #StatsSky

Martin Plöderl @ploederl.bsky.social · 4mo ago

1. The paper with the implausibly large effects of Omega-3 fatty acids on mental health was now retracted. A little thread on the process where @ianhussey.mmmdata.io and I was involved. www.sciencedirect.com/science/arti...

RETRACTED: The effects of Omega-3 supplementation on stress, anxiety, depression, sleep quality, and everyday memory in individuals with psychological distress: A randomized, double-blind, placebo-con...

This article has been retracted: please see Elsevier policy on Article Correction, Retraction and Removal (https://www.elsevier.com/about/policies-and…

sciencedirect.com

Martin Plöderl@ploederl.bsky.social · 7mo ago

Hurray, Omega-3 ends all mental suffering! Reduction of stress, anxiety, and depression with SMD > 3! Quite a lubricated review process, too: Received 8 Sep 2025; Revised 17 Nov; and accepted on Christmas (24 Dec). www.sciencedirect.com/science/arti...

Richard McElreath 🐈‍⬛@rmcelreath.bsky.social · 5mo ago

Statistical Rethinking 2026 is done: 20 new lectures emphasizing logical and critical statistical workflow, from basics of probability theory to causal inference to reliable computation to sensitivity. It's all free, made just for you. Lecture list and links: github.com/rmcelreath/s...

Mikki Brock @mikkibrock.bsky.social · 4mo ago

I don't see this said enough: the widespread use of generative AI is not only making our jobs as educators harder logistically, but also emotionally. It is genuinely sad to be suspicious of students when you have spent so much time building a pedagogy based on trust and not being a cop. It sucks.

Adrian Barnett @aidybarnett.bsky.social · 4mo ago

Very proud to have worked on the 2026 update to the CRediT taxonomy with @rbly.bsky.social

CRediT’s 12 Contributor Roles
Searching: Made a list of potential journals ordered by their impact factor
Formatting: Spent hours on fiddly and pointless journal formatting changes
Funding: Took out a personal loan to pay the APC
Analysis: Designed the analyses to find all possible statistically significant associations
Order: Bullied out of rightful author position
Gift: Gift authorship so that the first author can get promoted
Coasted: Didn't do anything, just happy to be included
Review: Responded to the reviewers using bluster and obfuscation
Writing: Delegated writing to AI — original draft
Editing: Delegated writing to AI — review & editing
Quid pro quo: Included a peer reviewer's recommended citations in exchange for journal acceptance
Conflict: Hid a financial conflict of interest

Miguel Hernan @miguelhernan.org · 4mo ago

Two common misconceptions when repurposing data for #causalinference: 1) the target trial is an ideal trial 2) the target trial protocol can be prespecified Our new paper examines how the target trial protocol depends on the causal question AND the available data. journals.lww.com/epidem/abstr...

Lisa DeBruine @debruine.bsky.social · 4mo ago

The @ukrepro.bsky.social just published a new primer on computational reproducibility, authored by the CR special interest group, including yours truly! I hope it's useful for introducing people to the concept and convincing resource-holders to fund efforts to improve it. zenodo.org/records/1923...

Richard McElreath 🐈‍⬛@rmcelreath.bsky.social · 4mo ago

how I feel when open science initiatives choose to publish in Nature

Batman breaking a firearm and saying, "This is the journal of the enemy. We do not need it. We will not use it."

Crystal Lewis @cghlewis.bsky.social · 6mo ago

It's not unheard of to find errors in your data after publishing it. While it's not fun when this happens, this one-pager can help guide you through the process of updating data, code, and publications when errors are found. osf.io/q4jre/files/...

Ian Hussey @ianhussey.mmmdata.io · 10mo ago

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

Chelsea Parlett @chelseaparlett.bsky.social · 10mo ago

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

Peter Tennant @pwgtennant.bsky.social · 11mo ago

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

Ryan Briggs @ryancbriggs.net · 11mo ago

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

Julia M. Rohrer @dingdingpeng.the100.ci · 12mo ago

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.

Peter Tennant @pwgtennant.bsky.social · 12mo ago

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 · 12mo ago

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.

Emily Riederer @emilyriederer.bsky.social · last yr.

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...

emilyriederer.com

Kevin C Klatt, PhD, RD @kcklatt.bsky.social · last yr.

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.

healthbeat.org

Ole Goltermann @olegolt.bsky.social · last yr.

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. 👇 1/13

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

The BMJ @bmj.com · last yr.

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

Jordan Nafa @ajordannafa.com · last yr.

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

Julia M. Rohrer @dingdingpeng.the100.ci · last yr.

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).

Andrew Mercer@awmercer.bsky.social · last yr.

Maybe use the same number of bins as you have knots in the spline? That’d make it easier to compare the two.

Didier Brassard @didierbrassard.bsky.social · last yr.

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...

‘Statistical method you should know’: restricted cubic spline

In this article, I describe and provide a brief introduction for astatistical method that I find very useful: restricted cubic splines.During my PhD, I diligently learned regression models assumption ...

didierbrassard.github.io

Gary Collins@gscollins.bsky.social · last yr.

NEW PAPER in @bmj.com "Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide" --> www.bmj.com/content/390/... #methodologymatters #StatsSky #EpiSky

Tom Yates @tomayates.bsky.social · last yr.

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

Richard McElreath 🐈‍⬛@rmcelreath.bsky.social · last yr.

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

Didier Brassard @didierbrassard.bsky.social · last yr.

📄 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...

medrxiv.org

Didier Brassard @didierbrassard.bsky.social · last yr.

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 · last yr.

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...