1/7 #PREPAREII showed that a 500-ml fluid bolus does not prevent cardiovascular collapse during tracheal intubation in the ICU. I love efficient study designs - this trial had two features that in my opinion are worth replicating in future #criticalcare#RCTs (a thread 🧵):
2/7 Sample size: the authors calculated that 750 patients would be needed to detect a MCID with an 80% power. After a blinded interim analysis it became clear that the event rate is lower than expected. Smart study design feature no.1️⃣: adaptive sample size re-estimation.
3/7 The goal of sample size re-estimation is to prevent the dreaded "underpowered study" - a situation where the trial can no longer rule out that a clinically meaningful effect exists due to the signal/noise ratio in the data being to low. You can do it using both frequentist &
4/7 Bayesian statistics. In PREPARE-II, the authors looked at the overall event rate without knowing how many cardiovascular collapses occurred in the intervention/control group. ⬆️ sample size is not statistical trickery - it saves 💰 otherwise wasted on an uninformative trial.
5/7 Smart study design feature no.2️⃣: heterogeneity of treatment effect (HTE) analysis showing how often we are mistakenly focused on “subgroup effects”. A picture tells a thousand words: can you point to any SBP & baseline risk “subgroups” on these graphs?
6/7 No - and you don’t need to do it! Both blood pressure measurements & risk estimates are continuous variables that should not be arbitrarily dichotomised (e.g. SBP <100 vs >100 mm Hg). #Dichotomania = throwing information away & ⬇️ power fharrell.com/post/errmed/