Randomization in clinical trials: simple, block, and stratified

Why randomize?

Randomization is the strongest defense against selection bias. In a well-executed RCT, baseline differences between groups are attributable to chance, not investigator decisions.

But randomizing well isn't flipping a coin. There are three standard methods, each with its context.

1. Simple randomization

Each subject has the same probability of being assigned to any group, independent of prior subjects.

• Pros: Trivial to implement. Sequence completely unpredictable.
• Cons: In small samples (<100) groups can be very imbalanced. With 30 subjects you might end up 18 vs 12.
• When to use: Large samples (>200) without strong prognostic covariates.

2. Block randomization

A block size is defined (typically multiple of the number of groups: 4, 6, 8 for 2 arms). Within each block, subjects are assigned so that groups are balanced once the block is complete.

• Pros: Maintains balance throughout recruitment. Avoids imbalances from early closure.
• Cons: If the investigator knows the block size, they can predict the last assignments. Mitigate with variable block sizes.
• When to use: The default for modern clinical trials.

3. Stratified randomization

Strata are defined based on important prognostic variables (age, site, sex, severity). Within each stratum, randomization (simple or block) is applied.

• Pros: Ensures balance in the most important covariates. Reduces estimator variance.
• Cons: More complexity. With many strata you can end up with very small groups.
• When to use: Multi-center, or when a known covariate affects the outcome and you want to control for it.

Reproducibility: the seed

Every randomization must be reproducible. In audit, you must be able to show exactly how each assignment was generated.

This is achieved by saving the random generator seed in the audit trail. With the same seed and the same algorithm, you recreate the exact sequence.

Trialinx stores the seed along with method and block size in every study's audit trail.

Quick checklist

• Is the randomization method predefined in the protocol? Yes.
• Is the seed documented? Yes.
• Is randomization blind to the investigator? Use a system that executes it without revealing the algorithm.
• Are stratification variables clinically justified? If >3, probably not.
• Can you recreate the full sequence from the seed? If not, it's not reproducible.

Conclusion

Randomization is one of the most powerful instruments in clinical research — and one of the easiest to implement poorly. Pick the right method for the sample size and context, document the seed, and let the system handle the rest.