Planning and ANalyzing OPTImal Clinical trials with Adaptive Design

使用自适应设计规划和分析最佳临床试验

• 批准号: 443177220
• 负责人: Professor Dr. Meinhard Kieser, Ph.D.
• 金额: --
• 依托单位: Institut für Medizinische Biometrie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/443177220?language=en
• 关键词: Planning; ANalyzing; OPTImal; Clinical; trials

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.

在计划临床试验时，样本量计算是一项重要的生物统计任务。自适应样本大小规则提供了一个有吸引力的替代标准规则的固定样本大小。在这种情况下，当样本量根据迄今为止收集的数据自适应时，中断正在进行的研究进行一次或多次中期分析。虽然这一一般想法似乎很直观，但关于样本量规则的“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样本量重新计算的合理有效规则。除其他外，还开发了统计编程语言R的软件包adoptr。它允许用户计算任意评分标准的最优设计。这对临床和方法学研究非常有帮助。一方面，自适应设计提供了选择样本量规则，该规则不能相对于所选评分标准进一步改进。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adoptr的范围，并提高其在临床研究中的质量。为了实现这一目标，预计将实现以下工作包：1.更多终点目前，adoptr只提供正态分布的测试统计量。这限制了具体的应用。例如，规划具有二元终点的临床试验（例如，成功率）或事件发生时间终点（例如，生存时间）可能是合理的延长。在adoptr中实现这样的场景可以增加其适用性。2.分析.除了计划之外，试验的最终分析也是非常重要的。然而，应用标准方法来分析固定样本量的设计是不足以适应性设计。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将涵盖临床试验中的整个生物统计任务范围。质量保证。特别是在临床试验的监管环境中，使用合格的软件非常重要。由于R是一种开源编程语言，因此需要明确的质量保证才能使其应用于临床研究。实现这一目标的第一步已经迈出。然而，这些既不完整，也不符合监管要求。因此，预计将包括adoptr的质量保证。