Planning and ANalyzing OPTImal Clinical trials with Adaptive Design

在规划临床试验时，样本量计算是一项重要的生物计量任务。自适应样本量规则为固定样本量的标准规则提供了一个有吸引力的替代方案。在这里，当样本量根据迄今收集的数据进行调整时，正在进行的研究被中断一次或多次中期分析。虽然这个想法似乎很直观，但关于样本大小规则的“最佳”选择的问题要复杂得多。在正在进行的dfg项目ORACLE中，开发了适应性试验设计中合理有效的样本量重新计算规则。其中，采用统计编程语言R开发了软件包。它允许用户计算任意评分标准的最佳设计。这对临床和方法学研究有很大的帮助。一方面，自适应设计提供了一个不能根据所选评分标准进一步改进的样本量选择规则。此外，用户对客观标准的特定选择允许对所产生的最佳设计进行统计调查。在拟议的DFG项目中，计划扩大研究软件采用者的范围，并提高其在临床研究中的使用质量。为实现这一目标，预计将有以下工作包：进一步的端点。目前，adoptr只提供正态分布的测试统计数据。这限制了具体的应用程序。例如，规划具有双终点（例如，成功率）或时间到事件终点（例如，生存时间）的临床试验可能是一个明智的扩展。在adoptr中实现这样的场景可能会增加其适用性。2. 分析。除了计划之外，对试验的最终分析也非常重要。然而，应用标准方法来分析具有固定样本量的设计是不足以适应设计的。因此，除了样本量规则之外，提供点估计量、置信区间和p值的计算将非常有益。因此，adoptr将涵盖临床试验中生物统计学任务的整个范围。质量保证。特别是在临床试验的监管环境中，使用合格的软件是非常重要的。由于R是一种开源编程语言，明确的质量保证是必要的，以允许其在临床研究中的应用。实现这一目标的第一步已经迈出。然而，这些既不完整，也不满足监管要求。因此，期望对采用者进行全面的质量保证。

Sample size calculation is an essential biometrical task when planning clinical trials. Adaptive sample size rules provide an attractive alternative to standard rules for fixed sample sizes. Here, the ongoing study is interrupted for one or several interim analysis(es) when the sample size is adaptive based on the data collected so far. While this general idea seems intuitive the question on the “best” choice of the sample size rule is much more complicated. During the ongoing DFG-project ORACLE, sensible and efficient rules for sample size recalculation in adaptive trial designs are developed. Among others, the software package adoptr in the statistical programming language R has been developed. It allows his user to compute the optimal design for an arbitrary scoring criterion. This is highly helpful for clinical and methodological research. On the one hand, adaptive designs provide choosing a sample size rule that cannot be further improved with respect to the selected scoring criterion. Furthermore, the user-specific choice of the objective criterion allows a statistical investigation of the resulting optimal designs.Within the proposed DFG project, it is planned to expand the scope of the research software adoptr and to increase its quality for the usage in clinical research. To this goal, the following working packages are anticipated:1. Further endpoints. Currently, adoptr only provides normally distributed test statistics. This limits a concrete application. For instance, planning clinical trials with binary endpoints (e.g., rate of success) or time-to-event endpoints (e.g., survival time) may be a sensible extension. An implementation of such scenarios in adoptr may increase its applicability. 2. Analysis. Besides planning, the final analysis of a trial is highly relevant. However, applying standard methods to analyze designs with fixed sample sizes is not adequate for adaptive designs. Therefore, it would be highly beneficial to provide the computation of point estimators, confidence intervals, and p-values besides sample size rules. Thereby, adoptr would cover the entire range of biostatistical tasks within clinical trials.3. Quality assurance. Particularly in the regulated environment of clinical trials, the usage of qualified software is of high importance. Since R is an open-source programming language, explicit quality assurance is necessary to allow its application in clinical research. First steps to achieve this goal have already been made. However, those are neither complete, nor do they fulfill regulatory requirements. Therefore, encompassing quality assurance of adoptr is anticipated.