Using Synthetic Controls to Improve Randomised Control Trials for Rare

使用合成对照改进稀有药物的随机对照试验

• 批准号: 2884936
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884936
• 关键词: Using; Synthetic; Controls; Improve; Randomised

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.

由于缺乏参与者、伦理和成本，通常难以执行用于罕见疾病治疗的RCT（Thorlund等人，2020年）。在这些RCT中，可能使用合成对照组代替安慰剂或对照组。本研究有两个目的：第一，确定哪些类型的历史数据在生成合成对照时是可接受的，以及这些方法之间的准确性差异;第二，创建一个机器学习模型，该模型可以在给定历史实验组的情况下预测对照组。将基于三种不同类型的历史数据（既往RCT、观察性研究数据和外部数据）生成单独的合成对照组，并与真实的历史对照组进行比较;第二个目标是，基于公开可用的汇总水平RCT数据的对照组和实验组模拟数据集将用于训练机器学习模型。还将从所有可用来源招募历史RCT用于模型的测试。首先，将使用现有软件包从以前的RCT、观察性研究和外部数据中生成合成对照。使用Pearson相关系数将合成对照与历史对照组和实验组进行比较。在第二个目标中，生成对抗网络（GAN）深度学习模型将用于从输入数据集生成控制数据。使用Pearson相关系数将生成的数据与试验数据进行比较。