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miguelhernan.org
Miguel Hernan
@miguelhernan.org
8.2K followers 120 following 33 posts
https://miguelhernan.org/ Using health data to learn what works. Making #causalinference less casual. Director, @causalab.bsky.social Professor, @hsph.harvard.edu Methods Editor, Annals of Internal Medicine @annalsofim.bsky.social
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Miguel Hernan @miguelhernan.org · Dec 23
Upgrade your #causalinference arsenal.

A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook

Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material.

Enjoy the #WhatIfBook plus code and data. Also, it's free.
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Reposted by Miguel Hernan
dvansanten.bsky.social
Daniela van Santen @dvansanten.bsky.social · Sep 4
Free Causal Inference Consulting Available at Harvard T.H. Chan School of Public Health!

Take advantage of expert advice for your research projects. Learn more and help spread the word! :)
causalab.bsky.social
CAUSALab @causalab.bsky.social · Sep 4
Fall applications are OPEN for CAUSALab Clinics!

Free #causalinference consulting open to Boston-based, junior clinical investigators. Postdoc fellows provide guidance pertaining to #studydesign, data analysis and results for works in progress.

Learn more & apply:
hsph.harvard.edu/research/cau...
Fall 2025 CAUSALab Clinics dates
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Sep 3
When using observational data for #causalinference, the choice isn’t between emulating or not emulating a #TargetTrial, but between reporting or not reporting the target trial that we are emulating.

For those who prefer to be explicit about what they do, we have developed the TARGET Statement 👇
hjhansford.bsky.social
hjhansford.bsky.social @hjhansford.bsky.social · Sep 3
🎯 TARGET Guideline published 🎉

TARGET is a reporting guideline for observational studies of interventions that use the target trial framework.

Over 3 years the @TARGETGuideline was rigorously developed and was co-published today in @jama.com & @bmj.com

doi.org/10.1001/jama.2025.13350

#episky
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Reposted by Miguel Hernan
causalab.bsky.social
CAUSALab @causalab.bsky.social · Jul 8
SER 2025 @societyforepi.bsky.social included a session spotlighting James M. Robins⭐

"Celebrating James M. Robins Contributions to Epidemiology" explored Robins' impact, including his landmark 1986 paper. It concluded with his comments on progress still to come in #causalinference research.
James Robins at SER 2025
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Reposted by Miguel Hernan
harvardepi.bsky.social
Harvard Epidemiology @harvardepi.bsky.social · May 23
New publication: Effect of colonoscopy screening on risks of colorectal cancer and related death: instrumental variable estimation of per-protocol effects now in the European Journal of Epidemiology

➡️ Read here: link.springer.com/article/10.1...

#cancer #screening #colorectal
Effect of colonoscopy screening on risks of colorectal cancer and related death: instrumental variable estimation of per-protocol effects - European Journal of Epidemiology
Background We recently reported per-protocol estimates of colonoscopy screening on colorectal cancer incidence and mortality in NordICC, a large-scale randomized trial. Our results may be affected by ...
link.springer.com
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Reposted by Miguel Hernan
causalab.bsky.social
CAUSALab @causalab.bsky.social · May 22
See you in Madrid?

CAUSALab is partnering w/ @cemfi.es for the course, Causal Inference for Health and Social Scientists.

📆 Aug 25-29, 2025

Taught by @miguelhernan.org, CEMFI course introduces 2 step causal framework for experimental & non-experimental data.

www.cemfi.es/programs/css...
CEMFI Summer School Course, "Causal Inference for Health and Social Scientists" (Aug 25-29, 2025)
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Apr 24
If you're wondering about differences between publicly-funded research in non-profit universities and
privately-funded research in for-profit companies, watch this:
www.youtube.com/watch?v=Ar0z...

The topic is the "de-extinction of the dire wolf", but the message applies beyond it. (Think "AI".)
They Didn't Make Dire Wolves, They Made Something…Else
YouTube video by hankschannel
www.youtube.com
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Apr 2
Barbra Dickerman, @joy-shi.bsky.social, and I have a new online course for anyone who wants to learn the basics of confounding adjustment for time-fixed treatments.

A must if you are considering CAUSALab's "Advanced Confounding Adjustment" course for time-varying treatments in the Summer.
causalab.bsky.social
CAUSALab @causalab.bsky.social · Apr 2
NEW in 2025:
⭐ Fundamentals of Confounding Adjustment (FCA)

Learn confounding adjustment in time-fixed settings & build a foundation for advanced methods. Self-paced course w/ video lectures & hands-on exercises.

Ready to join our FCA classroom? Register now:
causalab.hsph.harvard.edu/courses/
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Mar 9
Anyone interested in science in the U.S. should read this.

www.insidehighered.com/opinion/view...
Why the NIH cuts are so wrong (opinion)
Christopher Newfield writes that higher ed has a better counternarrative to share.
www.insidehighered.com
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Reposted by Miguel Hernan
harvardepi.bsky.social
Harvard Epidemiology @harvardepi.bsky.social · Feb 26
Join us on Wednesday, March 5th at 1:00pm EST for the Department's seminar series with Miguel Hernan speaking on "How to make people immortal and why it is not a good idea: Improving the causal analyses of healthcare databases"

➡️ Go to event page to register: hsph.harvard.edu/epidemiology...
Miguel Hernan headshot
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Miguel Hernan @miguelhernan.org · Feb 18
Roger:

You’ve been ridiculing my posts for years. However, you've never written a paper that presents a thoughtful criticism of our work. Would you consider engaging in a scientific exchange?

Also, a piece of advice: Stop embarrassing yourself and read our papers before posting about them.

Prou.
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Miguel Hernan @miguelhernan.org · Feb 18
2/ The #TargetTrial framework is a structured procedure to operationalize good practices for study design, data analysis, and reporting.

It avoids design-induced biases but not biases arising from data limitations, such as measurement error and insufficient information to adjust for confounding.
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Feb 18
1/ When using observational data for #causalinference, emulating a target trial helps solve some problems... but not all problems.

In a new paper, we explain why and when the #TargetTrial framework is helpful.

www.acpjournals.org/doi/10.7326/...
Joint work with my colleagues @causalab.bsky.social
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Feb 3
3. "Why use methods that require proportional hazards?"
@amjepi.bsky.social 2025
doi.org/10.1093/aje/...

The proportional hazards assumption is generally superfluous. We encourage the use of survival analysis methods that produce absolute risks and that don't require constant hazard ratios.
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Feb 3
2. "Why test for proportional hazards?"
@jama.com 2020
jamanetwork.com/journals/jam...

Several examples show that hazards aren't expected to be proportional because either the effect isn't constant or the selection bias isn't constant.

An exception: null effect of treatment (hazard ratio=1)
...
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Feb 3
1. "The hazards of hazard ratios"
EPIDEMIOLOGY 2010
journals.lww.com/epidem/fullt...

Hazard ratios have a built-in selection bias because of depletion of susceptibles. Also, reporting only hazard ratios is insufficient because we also need (adjusted) absolute risks for sound decision making.

...
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Miguel Hernan @miguelhernan.org · Feb 3
In a recent commentary, Mats Stensrud and I argue that the proportional hazards assumption is not only implausible but also unnecessary.
doi.org/10.1093/aje/...

Easy-to-implement survival analysis methods that don't rely on proportional hazards are typically preferred.

The argument in 3 steps 👇
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Miguel Hernan @miguelhernan.org · Feb 3
1/
If you were taught to test for proportional hazards, talk to your teacher.

The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile.

But there is more 👇
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Jan 6
2/
Immortal time may occur when individuals

1) are assigned to treatment strategies based on post-eligibility information or

2) determined to be eligible based on post-assignment information.

#TargetTrial emulation prevents it by synchronizing eligibility and assignment at the start of follow-up.
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Jan 6
1/

That "immortal time" is so frequent in survival analyses for #causalinference is fascinating.

Because "immortal time" doesn't exist in the data, *we* create it when misanalyzing the data.

Our new paper pubmed.ncbi.nlm.nih.gov/39494894/ summarizes why immortal time arises & how to prevent it.
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Dec 23
Upgrade your #causalinference arsenal.

A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook

Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material.

Enjoy the #WhatIfBook plus code and data. Also, it's free.
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Nov 26
Agree. Stephen Senn's "Seven myths of randomisation in clinical trials" pubmed.ncbi.nlm.nih.gov/23255195/ is a good place to start.

And the work by Jamie Robins and colleagues helped us understand "the curse of dimensionality" in high-dimensional settings (references in Chapter 10 of "What If").
Seven myths of randomisation in clinical trials - PubMed
I consider seven misunderstandings that may be encountered about the nature, purpose and properties of randomisation in clinical trials. Some concern the practical realities of clinical research on pa...
pubmed.ncbi.nlm.nih.gov
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Nov 26
In Chapter 10 of "Causal Inference: What If", we describe arguments for adjustment in randomized trials and refute some fallacies used to advise against adjustment.
www.hsph.harvard.edu/miguel-herna...

A practical challenge is how to incorporate adjustment into the design of #randomizedtrials.
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miguelhernan.org
Miguel Hernan @miguelhernan.org · Nov 26
When risk factors are imbalanced for non-chance reasons in #observational studies, we call it #confounding.

An interesting point is that, regardless of whether the imbalance results from chance or confounding, we are better off ADJUSTING for prognostic factors that are imbalanced between groups.
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Miguel Hernan @miguelhernan.org · Nov 26
Unsurprising. By definition, the 95% confidence interval of 5% of (perfect) trials isn't expected to include the true value of the effect.

Again: Of 20 randomized trials in which treatment truly has a null effect, the 95% CI of one of them isn't expected to include the null value. Just by chance.
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Miguel Hernan @miguelhernan.org · Nov 26
Vass M (PhD Thesis). Prevention of functional decline in older people. Faculty of Health Sciences, U of Copenhagen 2010, p.120.
(Thanks to Mikkel Zöllner Ankarfeldt for bringing this example to my attention.)

What happened? By chance, some risk factors were more common in the intervention group.
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