Onyi Arah, MD, DSc, PhD

Royal Statistical Society Publications Summary. Conventional analytic results do not reflect any source of uncertainty other than random error, and as a result readers must rely on informal judgments regarding the effect of possible bias...

So does this one:

rss.onlinelibrary.wiley.com/doi/abs/10.1...

#causalsky #causalinference

Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods Abstract. I present some extensions of Bayesian methods to situations in which biases are of concern. First, a basic misclassification problem is illustrat

This paper on Bayesian perspectives for bias analysis comes to mind: academic.oup.com/ije/article-...

This recent RCT of an "AI stethoscope" claims the technology "shows promise" for diagnosing cardiovascular conditions.

It does not.

It is a textbook example of the risks of conducting unprincipled 'per protocol analyses'. Once again, peer review at a major medical journal has failed.

🧵 1/

You should do a letter to the editor about this! If they refuse to accept it, try another journal.

Best practices for moving from correlation to causation in ecological research - Nature Communications Different scientific traditions offer seemingly disparate approaches to inferring causal relationships in ecological systems. This Perspective unifies the causal assumptions and methods from...

Excited to share our new paper in @natcomms.nature.com We synthesize causal discovery & inference approaches across traditions (regression adjustment, quasi-expts, SEMs, Granger causality, convergent cross-mapping, and more) into a unified workflow for ecologists. www.nature.com/articles/s41...

I can

Yellow, green and red colored book cover of John Green’s EVERYTHING IS TUBERCULOSIS: THE HISTORY AND PERSISTENCE OF OUR DEADLIEST INFECTION

Enjoying this powerful book #EverythingIsTB by @johngreensbluesky.bsky.social

My birthday 🎁 from someone special who remembered my days as a physician in Africa

Fight #Tuberculosis

#PublicHealth matters!

Reconsidering the graphical representation of propensity scores in causal diagrams I read with interest Mansournia et al.’s article ‘Balancing scores and causal diagrams’ [1]. While their effort to use directed acyclic graphs (DAGs) to il

How to draw propensity scores (PS) in DAGs? Some (me also) claim it is like "treatment -> PS <- covariates", since in order to compute PS we need both treatment and covariates. This view has confused me for so long, and now I think I was wrong. My letter here: track.smtpsendmail.com/9032119/c?p=...

😂

Ten-year findings from the Puberty Cohort: a sub-cohort within the Danish National Birth Cohort AbstractBackground. The age of puberty is declining. Earlier puberty has major implications due to associations with later physiological and psychiatric mo

Ten years of research within the unique DNBC Puberty Cohort has identified several prenatal and childhood biological and psychosocial factors associated with earlier puberty

#EpiSky #Lifecourse #PublicHealth

academic.oup.com/ije/article/...

Rethinking Economics, the movement changing how the subject is taught Born of student disquiet after the 2008 crash, the group says it is reshaping economists’ education

"“By demanding that economics education should be more pluralist, more ethically conscientious, more historically aware, and more oriented towards the real world, Rethinking Economics has exposed the staggering deficiency in the way economists are educated..." www.theguardian.com/environment/...

Worth expanding to a multi-perspective view from employers, graduates, epi program directors & faculty, etc. We would want to hear from epidemiologists in government, industry, etc.

Summer Session in Epidemiology at University of Michigan. Apply online at SUMMEREPI.ORG

Courses

#EpiSky #CausalSky #PublicHealth

Summer School with me (teaching Applied Sensitivity Analysis) and others? Check this out:

sse.sph.umich.edu/courses/

Gladys Mae West obituary: mathematician who pioneered GPS technology She made key contributions to US cold-war science despite facing huge barriers as a Black woman.

No joke: I got angry hate mail today for writing an obituary of a Black woman scientist—because the person felt she did didn’t deserve the recognition.

Which just makes me want to share it again: www.nature.com/articles/d41...

Whose ethics govern global health research? Ethical research must not exploit scarcity as an experimental variable.

"Ethical research requires that participant safety remains central, not subordinate to hypothesis testing....Vulnerability should never be seen as an opportunity to advance research at the expense of those it claims to serve." @natureportfolio.nature.com
www.nature.com/articles/d41...

😂 I‘m sure I’m just as old/young as Richard. Just happened to become an MD before doing a PhD.

I was also taught MLM by Goldstein and later Joop Hox

I was in medical training in the 90s 😊

#EpiSky #Pregnancy #COVID19 #Vaccine #EHR #ClaimsData

Vooraf besteld…

Assessing the Use of Medical Insurance Claims and Electronic Health Records to Measure COVID-19 Vaccination During Pregnancy - Drug Safety Introduction The safety and effectiveness of COVID-19 vaccination during pregnancy are commonly assessed using administrative health records, which may misclassify vaccine exposures. Objective The aim...

How well do electronic health records and health insurance claims capture COVID-19 vaccine doses in the US? And how does measurement error impact estimates of vaccine safety and effectiveness in studies using these data sources?

link.springer.com/article/10.1...

🎉🎊

May we shut the door on this dismal last quarter of the first quarter of the 21st century

May 2026 usher in health, prosperity, and peace

May we feel joy

May we hope and aspire

May we love, trust, and protect one another up

May we repair, rebuild and reimagine

#OneWorld #NewYear2026

May we shut the door on this dismal last quarter of the first quarter of the 21st century

May 2026 usher in health, prosperity, and peace

May we feel joy

May we hope and aspire

May we love, trust, and protect one another up

May we repair, rebuild and reimagine

#OneWorld #NewYear2026

😬 going for the comic relief?

Led by @m-coates.bsky.social

#StatsSky #CausalSky #EpiSky

Re. Prediagnostic Exposures and Cancer Survival: Can a Meaningful Causal Estimand be Specified?

…research questions and estimands should concur on and be explicit about the target population(s)

#Causalinference #CausalSky #CausalEstimands #TargetPopulation #EpiSky

journals.lww.com/epidem/fullt...

Making DAGs even more useful: using augmented causal diagrams to depict counterfactual, study design, measurement, analytical, and interventional features Since their mainstream introduction in the 1990s, causal diagrams, including directed acyclic graphs (DAGs), have been increasingly used to depict our caus

New paper for #causalinference folks in all fields

#DAGs

#CausalDiagrams

#CausalSky #StatsSky #EpiSky

academic.oup.com/ije/article/...

A key methodological challenge in observational studies with interference between units is twofold: (1) each unit's outcome may depend on many others' treatments, and (2) treatment assignments may exhibit complex dependencies across units. We develop a general statistical framework for constructing robust causal effect estimators to address these challenges. We first show that, without restricting the patterns of interference, the standard inverse probability weighting (IPW) estimator is the only uniformly unbiased estimator when the propensity score is known. In contrast, no estimator has such a property if the propensity score is unknown. We then introduce a \emph{low-rank structure} of potential outcomes as a broad class of structural assumptions about interference. This framework encompasses common assumptions such as anonymous, nearest-neighbor, and additive interference, while flexibly allowing for more complex study-specific interference assumptions. Under this low-rank assumption, we show how to construct an unbiased weighting estimator for a large class of causal estimands. The proposed weighting estimator does not require knowledge of true propensity scores and is therefore robust to unknown treatment assignment dependencies that often exist in observational studies. If the true propensity score is known, we can obtain an unbiased estimator that is more efficient than the IPW estimator by leveraging a low-rank structure. We establish the finite sample and asymptotic properties of the proposed weighting estimator, develop a data-driven procedure to select among candidate low-rank structures, and validate our approach through simulation and empirical studies.

"Low-rank Covariate Balancing Estimators under Interference"
Always neat to see CBPS in the wild

arxiv: arxiv.org/abs/2512.13944
#statssky #causalsky