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  • $\begingroup$ Thanks for your input. But then, why should we care about stratifying the randomization (which may be difficult from a logistic perspective) if we adjust the analysis anyway? $\endgroup$ Commented May 31, 2024 at 9:15
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    $\begingroup$ Right, it's not always possible to stratify because you may not have the information (say, gender) you need before you start the RCT. Then you just adjust for gender in the analysis. However, if you stratify you will generally have less variance in your estimate than if you just adjust after the fact. $\endgroup$ Commented May 31, 2024 at 10:07
  • $\begingroup$ Short answer: it’s more precise and more accurate .(lower variance). Also, another reason to stratify is to ensure proper sample sizes for subgroup analysis. This proportionate sampling ensures representativeness. An undergrad class I TA’d back in the day did a simple sample of student veterans and gender analysis couldn’t be done because N was too small. 1. ncbi.nlm.nih.gov/pmc/articles/PMC3444136. 2. sciencedirect.com/science/article/pii/…. $\endgroup$ Commented Jun 1, 2024 at 1:01
  • $\begingroup$ Those links do a decent job of explaining. But that undergrad class could have stratified by gender and oversampled female student veterans and then applied weight adjustments to compensate (if there’s a sufficient sample size for each stratum weighting can simulate proportional stratification, albeit less precisely.) But a proper representative sample has a frequency distribution for important variables that matches the population distribution. Stratification helps ensure that the least represented level/subgroup of a variable has a proper sample size and the rest are calculated from that. $\endgroup$ Commented Jun 1, 2024 at 1:10
  • $\begingroup$ So which is the advantage of adjusting? Or when should you adjust over stratify if you have the choice and possibility? Because I usually see adjustments for sex but not that many studies with stratification $\endgroup$ Commented Dec 8, 2024 at 22:10