iiinova — where your digital self keeps evolving

Reposted by Arthur Chatton

Institute for Replication @i4replication.bsky.social · 29/05/2026

1/ Can AI help researchers check whether published social science results actually reproduce? In our new PNAS paper, we tested this directly in the AI Replication Games: 288 researchers, 103 teams, and real replication packages from quantitative social science.

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Rita Hamad @ritahamad.bsky.social · 06/04/2026

Poverty is a key driver of health; our latest article reviews the growing literature on economic policies as an upstream solution. However, several policies have insufficient evidence, and data barriers remain: www.annualreviews.org/content/jour... @hsph.harvard.edu @irpwisc.bsky.social

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Julia M. Rohrer @dingdingpeng.the100.ci · 04/02/2026

New preprint! So, what's a multiverse analysis good for anyway?> With @jessicahullman.bsky.social and @statmodeling.bsky.social juliarohrer.com/wp-content/u...

What’s a multiverse good for anyway?

Julia M. Rohrer, Jessica Hullman, and Andrew Gelman

Multiverse analysis has become a fairly popular approach, as indicated by the present special issue on the matter. Here, we take one step back and ask why one would conduct a multiverse analysis in the first place. We discuss various ways in which a multiverse may be employed – as a tool for reflection and critique, as a persuasive tool, as a serious inferential tool – as well as potential problems that arise depending on the specific purpose. For example, it fails as a persuasive tool when researchers disagree about which variations should be included in the analysis, and it fails as a serious inferential tool when the included analyses do not target a coherent estimand. Then, we take yet another step back and ask what the multiverse discourse has been good for and whether any broader lessons can be drawn. Ultimately, we conclude that the multiverse does remain a valuable tool; however, we urge against taking it too seriously.

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Dan Quintana @dsquintana.bsky.social · 14/11/2025

Our paper on improving statistical reporting in psychology is now online 🎉 As a part of this paper, we also created the Transparent Statistical Reporting in Psychology checklist, which researchers can use to improve their statistical reporting practices www.nature.com/articles/s44...

Transparent and comprehensive statistical reporting is critical for ensuring the credibility, reproducibility, and interpretability of psychological research. This paper offers a structured set of guidelines for reporting statistical analyses in quantitative psychology, emphasizing clarity at both the planning and results stages. Drawing on established recommendations and emerging best practices, we outline key decisions related to hypothesis formulation, sample size justification, preregistration, outlier and missing data handling, statistical model specification, and the interpretation of inferential outcomes. We address considerations across frequentist and Bayesian frameworks and fixed as well as sequential research designs, including guidance on effect size reporting, equivalence testing, and the appropriate treatment of null results. To facilitate implementation of these recommendations, we provide the Transparent Statistical Reporting in Psychology (TSRP) Checklist that researchers can use to systematically evaluate and improve their statistical reporting practices (https://osf.io/t2zpq/). In addition, we provide a curated list of freely available tools, packages, and functions that researchers can use to implement transparent reporting practices in their own analyses to bridge the gap between theory and practice. To illustrate the practical application of these principles, we provide a side-by-side comparison of insufficient versus best-practice reporting using a hypothetical cognitive psychology study. By adopting transparent reporting standards, researchers can improve the robustness of individual studies and facilitate cumulative scientific progress through more reliable meta-analyses and research syntheses.

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Reposted by Arthur Chatton

Dan Quintana @dsquintana.bsky.social · 11/11/2025

“Google's strategic transformation into an AI-first company fundamentally conflicts with maintaining niche academic services like Scholar”

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Reposted by Arthur Chatton

Dr. Lutz Böhm @drlutzboehm.bsky.social · 05/11/2025

You are a Post-Padawan now!

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Reposted by Arthur Chatton

Saloni @scientificdiscovery.dev · 28/09/2025

In general I think it's hard to combat scientific misinformation when some of the best research is locked behind an academic paywall, while lots of nonsense gets published free for everyone to read in predatory journals.

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Julia M. Rohrer @dingdingpeng.the100.ci · 09/05/2025

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.

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Reposted by Arthur Chatton

Epidemiology Job Openings (EpiJobs) @epijobs.bsky.social · 05/05/2025

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

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Reposted by Arthur Chatton

Julia M. Rohrer @dingdingpeng.the100.ci · 16/04/2025

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/...

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...

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Reposted by Arthur Chatton

Rushani Wijesuriya @rush-099.bsky.social · 27/01/2025

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/...

onlinelibrary.wiley.com

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....

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Reposted by Arthur Chatton

Institute for Replication @i4replication.bsky.social · 22/01/2025

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!

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Reposted by Arthur Chatton

Clémence Leyrat @clemley.bsky.social · 18/12/2024

📣 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 🌇🇫🇷

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Maarten van Smeden @maartenvsmeden.bsky.social · 16/12/2024

NEW PREPRINT A detailed overview of 32 popular predictive performance metrics for prediction models arxiv.org/abs/2412.10288

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Pausal Zivference @pausalz.bsky.social · 27/11/2024

best I got for Schrodinger's regression

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"

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Donald Szlosek @dszlosek.bsky.social · 14/11/2024

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

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Maarten van Smeden @maartenvsmeden.bsky.social · 11/11/2024

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...

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Reposted by Arthur Chatton

Darren Dahly @statsepi.bsky.social · 08/11/2024

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...

statsepi.substack.com

A conversation on treatment effects

The trial statistician and the clinical investigator took a step back to admire their creation.

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Reposted by Arthur Chatton

Daniel Westreich @epidbydesign.bsky.social · 12/09/2024

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!!

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Darren Dahly @statsepi.bsky.social · 28/10/2024

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....

tandfonline.com

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...

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Reposted by Arthur Chatton

Pausal Zivference @pausalz.bsky.social · 15/10/2024

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...

ncbi.nlm.nih.gov

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...

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Darren Dahly @statsepi.bsky.social · 09/10/2024

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....

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Michael Bazaco @mcbazacophd.bsky.social · 16/06/2024

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.

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

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Christopher Madan @engra.me · 03/06/2024

"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...

link.springer.com

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...

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Reposted by Arthur Chatton

Julia M. Rohrer @dingdingpeng.the100.ci · 02/03/2024

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/...

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

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Daniel Westreich @epidbydesign.bsky.social · 28/02/2024

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 --

epidemiologybydesign.com

Epidemiology By Design

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Anne-Laure Boulesteix @boulesteixlaure.bsky.social · 19/02/2024

“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... 1/n

onlinelibrary.wiley.com

Special Collection: “Neutral Comparison Studies in Methodological Research”: Biometrical Journal

The Biometrical Journal publishes papers on statistical methods and their applications to life sciences, encompassing medicine, environmental sciences & agriculture.

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Tim Morris @timpmorris.bsky.social · 08/01/2024

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

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Richard Riley (R²) @richarddriley.bsky.social · 04/01/2024

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.... 1/n

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Darren Dahly @statsepi.bsky.social · 29/12/2023

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.

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Richard Riley (R²) @richarddriley.bsky.social · 07/12/2023

Here is a 1-page summary for your wall This talk is based on our Christmas article from 2022 t.co/y4GVFmTOfs

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Michael P. Grosz @mp-grosz.bsky.social · 01/11/2023

🎉 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...

osf.io

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Sherri Rose @sherrirose.bsky.social · 18/10/2023

I've been told my stats/ML work is readable as an insult. That if my methods are easy to understand, the work itself is perceived as easy. Writing technical work that is understandable is harder than making it incomprehensible! Being incomprehensible can also be a facade for lack of actual novelty

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Dr Will Ball @wpball.com · 28/09/2023

Great accessible paper on causal inference methods here. Absolutely brilliant R code in the associated github repo too github.com/ArthurChatton/CausalCookbook Chapeau @achatton.bsky.social @dingdingpeng.bsky.social One for Episky folks

github.com

GitHub - ArthurChatton/CausalCookbook

Contribute to ArthurChatton/CausalCookbook development by creating an account on GitHub.

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Julia M. Rohrer @dingdingpeng.the100.ci · 15/09/2023

New preprint! There are lots of interesting estimators that can be used to target causal effects, some of which aren't well-known in psychology. Arthur Chatton and I provide a (gentle) introduction to approaches commonly used in epidemiology: psyarxiv.com/k2gzp

The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization
Abstract:
Recent developments in the causal inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do we actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and non-interference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses...

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