Daniel
Bodden

Randomization is supposed to mitigate biases in clinical trials, but what if the sequence becomes predictable?

In small, open-label trials (common in rare diseases), investigators may be able to guess upcoming treatment assignments. This is because randomization procedures always trade off balance against predictability: the more they force equal group sizes, the easier the next assignment is to guess. This can subtly shift which patients end up in which group, inflating the chance of a false-positive result.

We investigated how this "allocation bias" interacts with group sequential designs, where interim analyses allow early stopping to make more efficient use of patient data. The takeaway is straightforward: the randomization method is the main driver of bias, not the choice of spending function, futility rule, number or timing of interim looks.

If you can't blind your trial, choose a less predictable randomization procedure. Permuted blocks with small block sizes are the most vulnerable.

In clinical trials, randomization helps ensure participants are assigned to treatment groups in a way that minimizes bias. Group sequential designs allow interim analyses at pre-defined points, making it possible to stop early for success or futility.

We explored how different randomization procedures impact statistical performance (type I error rate and power) in group sequential trials with very small sample sizes.

This paper discusses

• ▸ How different randomization procedures affect power
• ▸ How improper design can affect type I error control
• ▸ A practical framework for choosing a suitable randomization procedure for small sample group sequential trials