Nice Simpsons reference, but symmetry is misleading. Consider this analogy: If you invest $100 in the stock market, you cannot lose more than 100% relative to that amount, but you can gain more than 100%. Additional reasons for leaving as ratios in link.

academic.oup.com/aje/article-...

Can’t imagine how random nonpositivity would be illustrated on a DAG. Could deterministic nonpositivity be illustrated similar to selection?

We must hold AI tools to the same standards as any other clinical intervention. Strong evidence builds trust and supports responsible adoption.

3/3

Too many AI interventions lack high-quality evidence. Most studies so far have high risk of bias and few report patient-relevant outcomes or potential harms.

www.thelancet.com/journals/lan...

2/3

"People Profit from being ambiguous about their research goals"

Julia concludes by highlighting the need for structural change. Rigorous causal research takes time and thought. That's not possible if we're still expecting PhD students to publish 3-5 papers.

This 'dataset first' approach leads some scientists to conduct weak research because 'this is the best we can do in our data'.

If a dataset is inappropriate for a particular question, the best you can do is NOT use it.

It shouldn't be our job, as scientists, to be showcasing datasets.

Best wishes for a rapid and full recovery.

A good starting point is the seminal article about this metric in a healthcare context: pubs.rsna.org/doi/10.1148/...

Additional nuances are discussed here: www.ahajournals.org/doi/10.1161/...

Interesting situation. Perhaps the journal or Editorial Board has a policy to help guide?

Just like the phrase, “…results should be interpreted cautiously.” As if results should ever be interpreted recklessly.

I’m with you and advocate for further inquiry. I also agree that inference requires multiple sources, but the question is whether some studies are even useful for informing policy? Savitz wrote a nice article about the need for policy-relevant research.

academic.oup.com/aje/article/...

Sometimes the available studies are so flawed or do not address the question of interest well enough that meta-analysis is unwarranted and no amount of sensitivity analysis or post hoc remedies can redeem. In such cases, greater value to provide guidance about how to improve the quality of studies.

A meta-analysis is only as good as the included studies. Most studies in this meta-analysis were riddled with selection, exposure and outcome misclassification, and confounding biases. In addition, the standardized mean difference is problematic for meta-analysis.

pubmed.ncbi.nlm.nih.gov/38761102/

So nice to see ideas statisticians established years ago about prediction models making a resurgence in other contexts.

#StatsSky

www.jclinepi.com/article/S089...