Miguel Hernan

Bluesky @miguelhernan.org

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Followers

8.4K

Account age

2 yrs

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Known for

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.

488 views 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.

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)

170 views 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)

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 👇

97 views 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 👇

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.

69 views 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.

📊 Post engagement

Avg engagement / post

Engagement vs followers

Nov 2023

On Bluesky since

🔥 Top post: Upgrade your #causalinference arsenal. A revision of our book " · 488 likes + reposts

📊 Activity & format

Posting cadence

0.17 / week

A lower-frequency account — each post lands with more weight.

Content mix

Mostly images

Recent: 5 text · 7 image · 0 video.

Follower / following

71×

Follows 119 back. A strong ratio — an audience that follows them, not a follow-for-follow network.

Recent posts

View on Bluesky ↗

🔥 Top post 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 d… ★ 488

Two common misconceptions when repurposing data for #causalinference: 1) the target trial is an ideal trial 2) the target trial protocol can be prespecified Our new paper examines how the target trial protocol depends on the causal quest… ★ 29 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 complexi… ★ 20

New study: Small benefits and risks of COVID-19 vaccines in children in Madrid. Hospitalization risk very low in unvaccinated, lower in vaccinated. 6-11 years old: no myocarditis cases 12-17 years old: myocarditis risk very low in vaccin… ★ 19 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 abo… ★ 30 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 d… ★ 6 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 Adjustme… ★ 9 Anyone interested in science in the U.S. should read this. www.insidehighered.com/opinion/view... ★ 16

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… ★ 59

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 durin… ★ 97

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/394948… ★ 69

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 pl… ★ 170

🦋 Handle & authenticity

✅ Domain-verified

@miguelhernan.org

The handle is the domain miguelhernan.org. On Bluesky a domain handle proves the owner controls that website — so this is verifiably the real miguelhernan.org account, not an impersonator.

On Bluesky since

Nov 2023

An early adopter from the invite-only beta (before the Feb 2024 public launch) — an original, established account.

💡 Facts

🗓️Joined Bluesky in 2023 — 2 years ago.

👁️Averages 84 views per post.

📤Posts about 0.2× per week.

🕵️ Fake follower check

Estimated

81/100

Excellent Credibility score

97%

Real Real audience

Low Fake-follower risk

High Data confidence

Est. 97% real, active audience · Low fake-follower risk.
Strong engagement (~1.0% of followers engage each post) — an active, real audience.
Organic base — far more followers than accounts it follows.

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

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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🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

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Miguel Hernan has 8,434 followers on Bluesky.

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Miguel Hernan

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Known for

📊 Post engagement

📊 Activity & format

Recent posts

🦋 Handle & authenticity

💡 Facts

🕵️ Fake follower check

About

📸 Gallery

🔀 Audience overlap

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