🤖 AI Summary
This study addresses the challenges in early-phase clinical trials arising from uncertainty in effect size and nuisance parameters, which often lead to inaccurate sample size planning and over-enrollment in group sequential designs. The authors employ a two-stage group sequential framework that dynamically adjusts the sample size at interim analysis based on conditional power, and systematically compare—using the gsDesign R package—the expected sample size and statistical power of conditional power–based designs against more conservative group sequential approaches. Findings indicate that conditional power designs offer only marginal benefits under specific scenarios, whereas conservative designs generally demonstrate superior efficiency, robustness, and the added advantage of avoiding premature disclosure of interim treatment efficacy. These results provide empirical evidence and practical guidance for selecting appropriate group sequential designs in clinical trial settings.
📝 Abstract
The effect size and nuisance parameters needed to appropriately size a clinical trial are generally not adequately understood before a trial begins. Through pre-planned early stopping rules, a conservatively planned group sequential design can effectively adapt the sample size for a clinical trial for a new treatment that is ineffective, very effective or minimally effective. One potential issue with such designs is that a substantial number of patients can be enrolled after a data cutoff for an interim analysis while data are being entered, cleaned, analyzed and discussed. A strategy of re-estimating the sample size at an interim analysis based on conditional power has been proposed to reduce somewhat this enrollment overrun issue. A purported advantage sometimes claimed is a smaller up-front planned sample size than a conservatively planned group sequential design. We demonstrate derivation of 2-stage group sequential and conditional power designs using the gsDesign R package and suggest comparing designs with comparable power using expected sample size calculations. While there are cases where conditional power designs may have small advantages, it is quite easy to derive very inefficient conditional power designs. This, along with the fact that a conditional power design may reveal something about the interim treatment effect, will often leave a group sequential design as the design of choice.