(untitled) (Epidemiology, Biostatistics, and Public Health, 2022)
Background: adaptive clinical trial design has been proposed as a promising new approach to improve the drug discovery process. Among the many options available, adaptive sample size re-estimation is of great interest mainly because of its ability to avoid a large ‘up-front’ commitment of resources. In this simulation study, we investigate the statistical properties of two-stage sample size re-estimation designs in terms of type I error control, study power and sample size, in comparison with the fixed-sample study. Methods: we simulated a balanced two-arm trial aimed at comparing two means of normally distributed data, using the inverse normal method to combine the results of each stage, and considering scenarios jointly defined by the following factors: the sample size re-estimation method, the information fraction, the type of group sequential boundaries and the use of futility stopping. Calculations were performed using the statistical software SAS™ (version 9.2). Results: under the null hypothesis, any type of adaptive design considered maintained the prefixed type I error rate, but futility stopping was required to avoid the unwanted increase in sample size. When deviating from the null hypothesis, the gain in power usually achieved with the adaptive design and its performance in terms of sample size were influenced by the specific design options considered. Conclusions: we show that adaptive designs incorporating futility stopping, a sufficiently high information fraction (50-70%) and the conditional power method for sample size re-estimation have good statistical properties, which include a gain in power when trial results are less favourable than anticipated.