Miguel Hernan

Miguel Hernan

@miguelhernan.org

https://miguelhernan.org/ Using health data to learn what works. Making #causalinference less casual. Director, @causalab.org Professor, @hsph.harvard.edu Methods Editor, Annals of Internal Medicine @annalsofim.bsky.social

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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.

Miguel Hernan reposted

The 2026 CAUSALab Summer Courses on Causal Inference have come to a close 📝 CAUSALab was excited to host 4 unique courses @hsph.harvard.edu. This year's 330+ participants represented: 🏢 180+ organizations 🌎 48 countries 📍 30 U.S. states Thank you for an exciting two weeks of #causalinference!

Having trouble with time zero when using healthcare databases to emulate a #TargetTrial? See our review of procedures to align eligibility and treatment assignment in observational emulations. We use 3 target trials of increasing complexity and provide a decision diagram www.bmj.com/content/392/...

lnkd.inStarting right: aligning eligibility and treatment assignment at time zero when emulating a target trialThis article provides methodological guidance when emulating a target trial with longitudinal observational data by showing how to align eligibility criteria and treatment assignment at the start of f...

Miguel Hernan reposted

You're invited! ✉️ 20th Kolokotrones Symposium: “Acetaminophen During Pregnancy and Autism: What Does Causal Inference Take?" Details in comments. In-person limited to Harvard ID holders due to space restrictions. Online attendance free & public. Register: www.eventbrite.com/e/acetaminop...

Miguel Hernan reposted

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@causalab.org

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...

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

🎯 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

Miguel Hernan reposted

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.

Miguel Hernan reposted

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@causalab.org

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/

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.

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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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 👇

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 👇

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.

Does #randomization ensures balance of risk factors between groups? Consider this: In Denmark 860 individuals were randomly allocated to either intervention or control. Individuals were unaware of their allocation. No intervention took place. Mortality was higher in the intervention group (p=0.003)

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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.

Does #randomization ensures balance of risk factors between groups? Consider this: In Denmark 860 individuals were randomly allocated to either intervention or control. Individuals were unaware of their allocation. No intervention took place. Mortality was higher in the intervention group (p=0.003)

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.

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.

Join us today!

CAUSALab@causalab.org

Join us tomorrow, Tues 10/29 3:00pm ET for the Center for Suicide Research and Prevention (CSRP) & @causalab.bsky.social's Suicide Research Colloquium. This month's topic - #causalinference & #suicideprevention. Attend in-person: bit.ly/SRCsession6 Attend online: bit.ly/src_zoom

Miguel Hernan reposted

Congrats to Roger Logan on his retirement! Roger has worked as a CAUSALab Senior Research Scientist @harvardchanschool.bsky.social for 23 years. He has been a valuable team member & made major contributions to #causalinference research. Wishing Roger all the best in this new chapter! #publichealth

Miguel Hernan reposted

New research published in Annals of Internal Medicine challenges past scholarship on metformin. CAUSALab collaborator Yu-Han Chiu identified no increased risk for childbirth with major birth defects when compared w/ women who discontinued the drug. CNN article: www.cnn.com/2024/06/17/h...

This week I discussed methods for health technology assessment at HTAi. My main point: "Observational data (#RWD) can often be used to emulate a #TargetTrial, but we need more research to characterize questions that can only be answered by randomized trials." Let's learn the limits of #RWE.

Miguel Hernan reposted

CAUSALab has a new collaboration with Broad Institute of MIT & Harvard! @miguelhernan.bsky.social received a Multidisciplinary University Research Initiative (MURI) award as a project collaborator to advance intervention design decision-making via computational framework.

defense.govDepartment of Defense Announces Fiscal Year 2024 University Research Funding AwardsDOD announced $221 million in awards for basic defense-related research projects as part of the Multidisciplinary University Research Initiative program.

Have you ever been advised to state your hypothesis in your grant application? Sander Greenland and I argue that stating hypotheses is unnecessary. lnkd.in/eW4ccgae Who cares what you guess the qualitative answer is before doing the study? Just do the study. Your hypothesis is nobody's business.

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