Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Berlin, Germany.
Methods Inf Med. 2021 May;60(1-02):1-8. doi: 10.1055/s-0040-1721727. Epub 2021 Mar 1.
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.
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.
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.
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.
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.
充分的样本量计算对于设计成功的临床试验至关重要。一种解决参数假设在样本量计算中所需的规划难题的方法是在正在进行的试验中自适应调整样本量。这可以通过适应性分组序贯设计来实现。在预定的时间点，测试中期效果是否具有统计学意义。根据中期测试结果，试验要么停止，要么继续进行，并且可能需要重新计算样本量。
样本量重算规则在应用中存在不同的局限性，例如重算样本量的变异性较高。因此，目标是提供一种工具来克服这种性能限制。
样本量重算规则可以解释为观察到的中期效果的函数。通常，在相当任意选择的中期效果大小处，从第一阶段的样本量到最大样本量实现“跳跃”，并且之后曲线单调递减。这种跳跃是样本量变异性较高的原因之一。在这项工作中，我们研究了如何通过实现样本量的平滑增加来改善重算函数的形状。通过蒙特卡罗模拟评估设计选项。评估标准是单变量性能指标，如条件功效和样本量，以及将这些组件结合在一起的条件性能评分。
我们证明了平滑校正可以降低条件功效和样本量的变异性，并且可以提高对于中等和大标准化效果大小的最近发表的条件性能评分的性能。
基于模拟研究，我们提出了一种易于实施的工具，用于改进样本量重算规则。该方法可以与文献中描述的现有的样本量重算规则结合使用。
