‘Planning a method for covariate adjustment in individually randomised trials’
New paper with Sarah Walker, Fizz Williamson & Ian White

Thread about the story of this one 🧵👇
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doi.org/10.1186/s13063… This article focuses on three broad methods for covariate adjustment in individually-randomised trials (particularly binary outcomes):
1. Direct adjustment
2. Standardisation
3. Inverse-probability of treatment weighting

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This is a great point. People sometimes introduce trials as 'easy' because in the simplest cases they permit causal inference with very simple analyses. This is never true in observational settings afaik. But the trials-are-simple message can deceive…
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https://twitter.com/ADAlthousePhD/status/1248321233877258240

When you get into it, the analysis of trials is just never simple.
Did you restrict the randomisation procedure by centre? Then you'd better reflect this in the analysis. How? Will you use fixed or random intercepts? Something else?
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Quick thread meandering into a weird DAG for trials. Here are three DAGs. The first two are uncontroversial but I've never seen the third depicted as a DAG.
1/ I recently read a description of a 'conditionally-randomised experiment', purposefully introducing different probabilities of treatment Z conditional on the value of binary covariate X.

*No-one does this but the idea was to introduce the idea of conditional exchangeability

The final ‘ICH E9(R1) Addendum on estimands and senstivity analysis in clinical trials’ was released on 3Dec2019.
database.ich.org/sites/default/… Work on randomised trials? Talk about trials? Think about trials? This is IMO the most important document of the last two decades.
And, ‘While the main focus is on randomised clinical trials, the principles are also applicable for single arm trials and observational studies.’

If you are about to tweet something in vague support of trials, STOP THERE!

Before you tweet that vague support, read this handy checklist of what might/will happen and ask yourself, ‘Is this how I want to spend today?’ 1. A person-bot who always triumphally exclaims ‘real-world evidence’ will triumphally exclaim ‘real-world evidence’ (+hashtag #RWE), as though trials use fake-world people.

I've seen so many people reference/tweet @_MiguelHernan's seminal paper The Hazard of Hazard Ratios and, to my shame, only just got to reading it.
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ncbi.nlm.nih.gov/pmc/articles/P… Aside: twitter has felt a bit less informative and more negative recently, and I miss people's fun threads/tweetorials so here's a thread.
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Hot off the press: I’m excited to share the results of The KMunicate study with you!

This one is on your very favourite topic: Kaplan–Meier* plots.
*Yes, with an en-dash
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bmjopen.bmj.com/content/9/9/e0… You all know what a Kaplan–Meier plot is? Yeah you do. You use them with time-to event data when you want an estimate of the cumulative proportion ‘surviving’ over time, and you plot it.
Here’s one from the RT01 trial.
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|￣￣￣￣￣￣￣￣￣￣|
ITT EFFECTS ARE OF
INTEREST TO
PATIENTS TOO!
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(•ㅅ•) ||
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Thread (with apologies to ‘my nemesis’ (not really) @EpiEllie for stealing the #epibunny
0/ So: ITT estimands are of interest to patients too!
If patients have told you otherwise, you’ve done a bad job of explaining to them (or thinking yourself about) what an ITT estimand is.
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