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Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data

机译:结合早期结果数据的II / III期无缝试验的数据驱动治疗选择

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Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.
机译:无缝的II / III期临床试验分两个阶段进行,第一阶段为治疗选择。在第一阶段,将患者随机分配至对照组或k> 1个实验治疗方案之一。在此阶段结束时,将分析临时数据,并决定哪种实验方法应继续进行到第二阶段。如果主要终点仅在一段时间的随访之后才可观察到,那么在中期分析中,可能会有一些早期结果可用的数据要多于主要终点可用的患者。这些早期终点数据因此可以用于治疗选择。对于两种先前提出的方法,已显示一种或多种方法的功效更大,这取决于真实的治疗效果和相关性。我们提出了一种新方法,该方法基于先前提出的方法,并使用中期分析中可用的数据来估计这些参数,然后在这些估计的基础上,选择最有可能正确选择最有效治疗方法的治疗选择方法。与广泛描述的两种真实参数值方法相比,该方法显示出与两种前述方法相比性能良好。在大多数情况下,新方法的性能类似于或在某些情况下优于之前提出的两种方法。

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