Stratification, block-randomization, etc

randomization

#1

Say I’m planning a randomized trial (time-to-event outcome) with 4 sites and want to balance randomization for each site. Am I then committed to stratifying by site in analysis? Or including site as a fixed effect in regression models?

My understanding is: stratified randomization -> stratified analysis
and block-randomization for balance —> consider factors as fixed effects

Balance is obviously good, and stratification may be necessary if baseline hazards have a very different shape. But as you stratify by more and more factors, moving toward matched-pairs randomization, the large number of strata seems to decimate study power.

I’m available with the literature pointing out drawbacks of matched case-control studies (Pepe et al, Clin Chemistry 2012). Are there any manuscripts or texts that lay out these issues for trial design and analysis?


#2

yes, according to ich e9 section 5.7 [ICHE9]

stephen senn has written about randomisation and balance eg when he was respondiong to that paper in social sci and medicine: "Perfect balance is not, contrary to what is often claimed a necessary requirement for causal inference, nor is it something that randomisation
attempts to provide. " [blog post]


#3

To add further to what Paul nicely stated, Senn has stressed that the analysis model should dictate the execution of the randomization and not vice-versa. And I think that blocked randomization is overused. The only real reasons I can think of blocking within center are

  • it’s hard to maintain blinding if you don’t
  • you don’t want to induce any kind of calendar time effect on outcomes if randomizations at one center end up being AAABBB

I’d love to hear more thoughts about that.