Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data - Harden, Markus; Friede, Tim (Biometrical journal, 2020)
Many late‐phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power‐loss compared to a more homogeneous single‐center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. The procedure estimates nuisance parameters at interim from noncomparative data and recalculates the sample size required based on these estimates. In contrast to other sample size calculation methods for multicenter trials, our approach assumes a mixed effects model and does not rely on balanced data within centers. It is therefore advantageous, especially for sample size recalculation at interim. We illustrate the proposed methodology by a study evaluating a diabetes management system. Monte Carlo simulations are carried out to evaluate operation characteristics of the sample size recalculation procedure using comparative as well as noncomparative data, assessing their dependence on parameters such as between‐center heterogeneity, residual variance of observations, treatment effect size and number of centers. We compare two different estimators for between‐center heterogeneity, an unadjusted and a bias‐adjusted estimator, both based on quadratic forms. The type 1 error probability as well as statistical power are close to their nominal levels for all parameter combinations considered in our simulation study for the proposed unadjusted estimator, whereas the adjusted estimator exhibits some type 1 error rate inflation. Overall, the sample size recalculation procedure can be recommended to mitigate risks arising from misspecified nuisance parameters at the planning stage.