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:
Next up, @leskocar on timing for loss to follow-up: when is someone actually lost to follow-up & when should we actually censor those who are lost?

Now for Ashley Buchanan who’s going to be talking about a fun project I’ve been working with her on about defining & estimating potential outcomes from an agent-based model for HIV infection using a #targettrial!
Our #targettrial is a complicated one: a two-stage, cluster randomized trial for PrEP initiation among sexual networks👇🏼

Using this framework helps us define the causal estimands of interest for the agent-based model *and* ensures we’ve been explicit about our target population.
Next up, @AudreyMurchland explains problems with instrumental variables for studying social determinants of health & how to improve them!

#ser2019 #SDOH
I’m up next, so hopefully someone else is also live tweeting. But I’ll also post my slides sometime later this week.

I should have made a slide gif to tweet now but ... hindsight is 20/20 & all that 🤷🏼‍♀️🤷🏼‍♀️
And finally, @ashley_naimi explains how to get causal effect estimates from machine learning algorithms!

To paraphrase: “With doubly robust methods & relatively mild conditions, we can estimate the average treatment effect with machine learning!!”
Important caveat: this means don’t use machine learning algorithms with single-robust methods!

Second important caveat: even with doubly-robust methods, to get good confidence interval coverage you need to use sample splitting (and even then it might not be super great 😬)
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