Using Synthetic Controls to Improve Randomised Control Trials for Rare

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.