Background An adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.

This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation.

Objectives Sample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation.

Methods Sample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a “jump” from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components.

Results We demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes.

Conclusion Based on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.

背景 充分的样本量计算对于设计成功的临床试验至关重要。解决与样本量计算所需的参数假设有关的计划困难的一种方法是在正在进行的试验期间调整样本量。

这可以通过自适应组序贯研究设计来实现。在预定义的时间点，测试中间效应的重要性。根据中期测试结果，试验要么停止，要么继续进行，并有可能重新计算样本量。

目标 样本量重新计算规则在应用中具有不同的限制，例如重新计算的样本量的高度可变性。因此，目标是提供一种工具来抵消这种性能限制。

方法 样本量重新计算规则可以解释为观察到的中间效应的函数。通常，从第一阶段的样本量“跳跃”到相当任意选择的中间效应量的最大样本量，然后曲线单调下降。这种跳跃是样本量高度可变的原因之一。在这项工作中，我们研究了如何通过更平滑地增加样本量来改进重新计算函数的形状。设计方案通过蒙特卡罗模拟进行评估。评估标准是单变量性能度量，例如条件功效和样本量以及结合这些组件的条件性能得分。

结果 我们证明，平滑校正可以减少条件功效和样本量的可变性，并且它们相对于最近发布的中型和大型标准化效应大小的条件性能得分提高了性能。

结论 基于模拟研究，我们提出了一种易于实施的工具，用于改进样本量重新计算规则。该方法可以与文献中描述的现有样本大小重新计算规则相结合。