Discover and read the best of Twitter Threads about #targettrial

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My next session on methods for complex data is happening now in Nicollet A!

I’ll be talking about pragmatic trials & per-protocol effects.
But first, @JessiePBuckley for Alex Keil on a new g-computation approach for causal effects of exposure mixtures.

One benefit over existing approaches: unlike weighted quartile sums regression, this method doesn’t assume all components work in the same direction.
There’s a preprint, so I’ll for sure be checking that out! The link is: arxiv.org/abs/1902.04200
Read 10 tweets
Our #ACIC2019 session is all about the #targettrial framework!

I kicked things off with a discussion of what causal effect we should estimate & the implications for our analyses.

Here’s a gif of my slides!
Next up, @lucia_petito gives a nice example of implementing the #targettrial framework to emulate an #RCT using electronic health care data.

One important concern: immortal time bias! 🧛🏻‍♀️🧛🏼‍♂️🧛🏻‍♀️🧛🏼‍♂️
Our next #targettrial example is from @elliecaniglia.

She explains some of the challenges of looking at the effect (if any) of statins on preventing dementia among older adults.

When time zero isn’t fixed, try emulating a sequence of target trials.
Read 5 tweets
I love fries; you love fries; but should we only eat 6 per serving? Less is more is good advice but why 6 & not 5, or 7?

Unfortunately, there’s prob no way to know if 5 or 6 is better!

Why? Here’s a #tweetorial on estimating causal effects for nutrition. Grab a 🥗 & get comfy!
Imagine you want to reduce your intake of French fries with the specific goal of reducing your chance of a heart attack.

You need to make 2 decisions: how often should I eat any fries; and how many fries should I eat in a serving?
To help you live your best life (ie eating max safe # of fries), researchers need to ask a pair of causal questions:

•what is the best frequency of French fry consumption to prevent heart attacks?
•what is the best serving size of French fries to prevent heart attacks?
Read 27 tweets
Results of the #TARGETtrial, the largest critical care nutrition trial ever undertaken, are now online @NEJM
nejm.org/doi/full/10.10…
What we did, what we found, and what it means follows…
Please RT to help translate this new knowledge.
Thanks to funding from @HRCNewZealand & @NHMRC we randomised 4000 participants from 46 Australian and New Zealand ICUs in less than a year and a half!
Adults mechanically ventilated & expected to require enteral nutrition in ICU beyond the calendar day after randomisation were assigned to energy dense enteral nutrition (1.5kcal/mL) or standard care enteral nutrition (1.0kcal/mL) at a dose of 1mL/kg/hr based on ideal body weight
Read 27 tweets
Judea Pearl has a new book (with Dana Mackenzie).
amazon.com/Book-Why-Scien…

To me, Judea is an intellectual hero. My life changed after hearing him at Harvard over 20 years ago. Like many of us in #causalinference, I owe so much to him.

And yet I disagree with him on a key issue.
Pearl believes that any causal effect we can name must also exist.

To him, the meaning of “the causal effect of A on death” is self-evident. He says we can quantify, say, the causal effect of race or the causal effect of obesity.

I don't think we can.
We cannot estimate "the causal effect of obesity" because we don't know what that means.

For the causal effect of A to be well defined, we need a common understanding of the interventions that we would use to change A. Otherwise, the effect is undefined.
ncbi.nlm.nih.gov/pmc/articles/P…
Read 6 tweets

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