Blinded and unblinded sample size re-estimation in crossover trials balanced for period
Michael Grayling, Adrian Mander, James Wason

TL;DR
This paper develops and compares blinded and unblinded sample size re-estimation methods for multi-treatment crossover trials, ensuring accurate variance estimation while maintaining blinding, and demonstrates their effectiveness through simulations.
Contribution
It introduces unbiased blinded estimators for variance components in crossover trials and compares their performance with unblinded methods.
Findings
Blinded estimators are unbiased with balanced sequences.
Performance of blinded methods is comparable to unblinded approaches.
Proposed methods are effective in practical trial scenarios.
Abstract
The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under- or over-powered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re-estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multi-treatment crossover trials. Specifically, regulators favour re-estimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomisation. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for…
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