Using Synthetic Controls to Improve Randomised Control Trials for Rare

由于缺乏参与者、伦理和成本，通常很难对一种罕见疾病进行随机对照试验(Thorlund等人，2020年)。在这些随机对照试验中，合成控制臂有可能用来代替安慰剂或控制臂。这项研究有两个目的：第一，确定哪种类型的历史数据在生成合成对照时是可接受的，以及两种方法在准确性上的差异；第二，建立一个机器学习模型，可以预测给定历史实验手臂的控制武器。将根据三种不同类型的历史数据(先前的RCT、观察性研究数据和外部数据)生成单独的合成控制臂，并将其与真实的历史控制臂进行比较。对于第二个目标，将使用基于公开可用的汇总级RCT数据的控制武器和实验武器的模拟数据集来训练机器学习模型。还将从所有可用的来源招募历史随机对照试验来测试模型。在第一个目标中，现有的程序包将用于根据以前的随机对照试验、观察性研究和外部数据生成合成对照。使用皮尔逊相关系数，将合成对照与历史对照和实验对照进行比较。在第二个目标中，将使用生成性对抗性网络(GAN)深度学习模型来从输入数据集生成控制数据。生成的数据将使用皮尔逊相关系数与测试数据进行比较。

It is often difficult to execute a RCT for a rare disease treatment due to lack of participants, ethics, and cost (Thorlund et al., 2020). Synthetic control arms could potentially be used in place of placebo or control arms in these RCTs. There are two aims of this study: first, to determine which types of historical data are acceptable in generating synthetic controls and what the difference in accuracy is between the methods, and second, to create a machine learning model that can predict control arms given historical experimental arms.For the first aim, historical RCT trials with IPD (individual participant data) will be used. Separate synthetic control arms will be generated based on three different types of historical data (previous RCTs, observational study data, and external data) and compared to the real historical control arm. For the second aim, simulated datasets of control arms and experimental arms based on publicly available summary level RCT data will be used to train the machine learning model. Historical RCTs will also be recruited from all available sources for the testing of the model.In the first aim, existing packages will be used to generate synthetic controls from previous RCTs, observational studies, and external data. Synthetic controls will be compared to historical control arms and experimental arms using Pearson's correlation coefficient. In the second aim, a generative adversarial network (GAN) deep learning model will be used to generate control data from input dataset. The generated data will be compared to the test data using Pearson's correlation coefficient.