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Luna Fazio

@lunafazio.bsky.social

18 followers 68 following 1 posts

allegedly, a researcher in bayesian stats (toolmaker is probably more accurate) lunafazio.github.io

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Reposted by Luna Fazio

Jörg Ankel-Peters @jrgptrs.bsky.social · 2d

New DP @i4replication.bsky.social: Meta-analysis on green nudges correcting for publication bias. "Behavioral interventions on households and individuals are unlikely to deliver material climate benefits." www.econstor.eu/bitstream/10...

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Reposted by Luna Fazio

Julia M. Rohrer @dingdingpeng.the100.ci · 9d

Just finished reading this *excellent* article by Gabriel et al. which discusses which effects can be identified in randomized controlled trials. With DAGs!>

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

Elucidating some common biases in randomized controlled trials
using directed acyclic graphs

Although the ideal randomized clinical trial is the gold standard for causal inference, real randomized trials often suffer
from imperfections that may hamper causal effect estimation. Stating the estimand of interest can help reduce confusion
about what is being estimated, but it is often difficult to determine what is and is not identifiable given a trial’s specific
imperfections. We demonstrate how directed acyclic graphs can be used to elucidate the consequences of common imperfections,
such as noncompliance, unblinding, and drop-out, for the identification of the intention-to-treat effect, the total
treatment effect and the physiological treatment effect. We assert that the physiological treatment effect is not identifiable
outside a trial with perfect compliance and no dropout, where blinding is perfectly maintained

Table 1 showing the Identifiability of target estimands depending on whether there is blinding, full compliance, and no drop-out

An example DAG from the paper.
Fig. 4: A blinded trial with noncompliance.

U are unobserved confounders, Z is treatment assignment, C is compliance, X is the realized treatment, S is the subject's physical and mental health status, Xself and Xcln are the treatment that the participant and the clinician believed the participant received, Y is the outcome.

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Luna Fazio @lunafazio.bsky.social · 9d

Took part yesterday in @freieuniversitaet.bsky.social's Replication Games, it was such a nice experience! People were lovely and the work was a very fun challenge.

Thanks to @i4replication.bsky.social and @janmarcus.de for organizing this, it was absolutely worth enduring the ride from Dortmund. 😄

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Reposted by Luna Fazio

Institute for Replication @i4replication.bsky.social · 10d

🚨I4R is looking to hire postdoc fellows in public health and computer science!

The postdoc will join a team of researchers and help mass reproduce studies in leading public health journals or develop AI replicator agents.

Info 👇

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Reposted by Luna Fazio

Jörg Ankel-Peters @jrgptrs.bsky.social · 24d

I4R Replication Games coming to Münster! @i4replication.bsky.social @aufdroeseler.bsky.social @fialalenka.bsky.social

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