@f2harrell.bsky.social you were right. Coverage for TMLE xgboost grid search size 5 appeared better than 20 but still the coverage is disappointing. I’ve updated this examination for my learning. Thanks again for the guidance. www.kenkoonwong.com/blog/tmle/

This is excellent!

😤

I 100% agree on the different hypothesis, I almost wrote a second reply to myself mentioning this (unless very strict assumptions apply). As for confidence intervals check out the Hodge-Lehman Estimator

stats.stackexchange.com/questions/19...

Also if you can wait 12+ hours on moderate sample sizes

I'm sure you have seen this but Katherine Hoffman wrote an excellent blog post on TMLE: www.khstats.com/blog/tmle/tu...

Interesting read! In spirit it reminds me of the Vibration of Effects work by Patel (2015) and colleagues, although their work was solely focused on analytical choices.

My knee jerk reaction is to almost always go with clopper-pearson (exact) mostly because the coverage is so good near 1 and 0 (or at least that is what I remember reading in a paper many years ago).

I was recently working with a distraught med student who told me one of his classmates had 75 (!!) publications!

I have wondered about this exact thought! Superb. Also love the Genstat output

my chest hurts reading this.

I would be very curious to hear @maartenvsmeden.bsky.social thoughts on handling large volume on prognostic model comparison. Single database? Just smaller time window? I think I remember some justification of think in a PROSPERO doc of yours.

Exactly my thought re: multi-state model and msm package I mentioned earlier.

Is aging out a competing risk? Outside of LTMLE, multistate models (MSM package in R) + IPCW would work well here if I am understanding the question correctly.

If they check back every month to assess complexity (cpx) you could treat cpx as a time varying covariate and run something like Longitudinal Targeted Maximum Likelihood Estimation. You would essentially IPW at each check in to reweight cpx. Time to event would to time to surgery ~ defer time.