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/ 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

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 👇

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.

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.

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)

It's always a good time to remember Brandolini's principle
@ziobrando.bsky.social

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.

Did you know that the LATE estimator was independently described in 1994 by

Imbens & Angrist in Econometrica

and

Baker & Lindeman in Statistics in Medicine?
onlinelibrary.wiley.com/doi/10.1002/...

A delightful historical overview of LATE is now available www.tandfonline.com/doi/full/10....