For multidrug-resistant #tuberculosis, the optimal duration of bedaquiline treatment is unknown.

Our #TargetTrial emulation: Bedaquiline for more than 6 months doesn't increase the probability of success.

Cloning+censoring+#IPweighting

https://www.atsjournals.org/doi/10.1164/rccm.202211-2125OC

Observational studies are often the only option to
estimate effects of interventions during #pregnancy.

Thinking of emulating a #TargetTrial during pregnancy using healthcare databases?

Here we review challenges and propose solutions to common biases 👇
https://pubmed.ncbi.nlm.nih.gov/36722806/

Because randomized trials are great only when they exist.

Without trials, high-quality observational data + explicit #TargetTrial emulation is our best chance to help

people make the treatment decisions they must make

until we convince companies/funders to pay for the trial.

1/

Problem: The best therapies for advanced pancreatic #cancer (Folfirinox, GN) haven't been compared in a head-to-head randomized trial due to cost and commercial barriers.

We emulated a #TargetTrial using observational data: Folfirinox is a bit better

https://www.sciencedirect.com/science/article/pii/S1047279722003088?dgcid=coauthor

I just spent the last hour with a brilliant young anesthesiologist imagining potential good questions in management of #IntraoperativeHypotension in #CardiacSurgery

I used @SPT_MD's example of specification and emulation of a #TargetTrial to help really clarify the questions of interest, and let me tell you, @SPT_MD's way of thinking is great

1/
Our summary of #TargetTrial emulation is out.

We illustrate it with another observational success:

A survival benefit of #tocilizumab in critically ill #COVID19 patients was quantified using observational data before a randomized trial confirmed it.
👇

https://jamanetwork.com/journals/jama/fullarticle/2799678

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Our summary of #TargetTrial emulation is out.

We illustrate it with another observational success:

A survival benefit of #tocilizumab in critically ill #COVID19 patients was quantified using observational data before a randomized trial confirmed it

https://jamanetwork.com/journals/jama/fullarticle/2799678

My #introduction:

I repurpose observational #RealWorldData into scientific evidence for the prevention and treatment of human disease. At #CAUSALab, we often do so by explicitly emulating a #TargetTrial. Other times we analyze #RandomizedTrials.

I teach #causalinference methods at the #Harvard T.H. Chan School of #PublicHealth. My online course #CausalDiagrams and “Causal Inference" #WhatIfBook (with James Robins) are free. See my profile.

#epidemiology #epiverse
#statistics
#datascience
#AI

4/
The key point:

Explicit #TargetTrial emulation prevents mistakes in observational data analyses that we wouldn't make in analyses of randomized trials.

That we can do something with the data doesn't imply that the result is causally interpretable.

More here:
https://www.nejm.org/doi/10.1056/NEJMp2113319

If you're looking for a gentle introduction to #causaldiagrams, #selectionbias, and colliders
enjoy our free course
"Causal Diagrams: Draw Your Assumptions Before Your Conclusions"
https://www.edx.org/course/causal-diagrams

2/
The critics' own words below.

What a wonderful opportunity to highlight the advantages of #TargetTrial emulation using observational data.

Note the analysis of randomized trials of vaccines is NOT restricted to people who get infected during the study.

Why should the analysis of observational studies be?

Here are 2 reasons why we never restrict the analysis of a #TargetTrial (real or emulated) to people who get infected after assignment to vaccine/no vaccine.