Synthetic Control Arms (SCAs) are a powerful tool in clinical trials. This is particularly true in cases where traditional control arms are difficult to implement. This could be due to ethical concerns, logistical challenges or limited patient availability.
Either way, synthetic control arms can be the ideal solution. But what is a SCA and how can you use these in clinical trials for the best possible results?
Well, that’s exactly why we’ve created this guide, here is how and why you should use a synthetic control arm in your study.
Understanding synthetic control arms - What is it?
An SCA is a statistically constructed control group that is created using data from previous clinical trials, observational studies or real-world evidence instead of enrolling new patients into a control arm.
Essentially what this means is that a group is constructed from individuals who are not part of the same study as the new treatment group.
The purpose of this is to reduce the number of participants needed in the control group, which is particularly useful in rare diseases. This can also be useful for personalised medicines or when the standard of care is already well-established, but more on this later in the guide.
How to use SCAs
Now we know what SCAs are, how can you use these in your trial? Well, here are the four key steps to getting it right.
1. Choose data from the right sources
It’s important that you are able to find the right data to ensure the success of your future studies or trials. The best place to start is looking at historical clinical trial data from previously conducted trials on similar patient populations with the same or similar treatments.
You can also gather real-world data from electronic health records (EHRs), registries, insurance claims or patient-reported outcomes, provided you have access to or are allowed to do this, of course.
Then there are observational studies where patients are observed under standard care without intervention and this data can be gathered and analysed outside of an official clinical trial.
By ensuring that you get the right data from the right sources, you’re more likely to achieve accurate, detailed results that answer the initial question or hypothesis.
2. Design the trial with a SCA right from the start
Designing clinical trials is no small task and if you are planning on using a SCA, you need to implement this into the plan and protocol right from the start.
So, as well as choosing robust and relevant data sources, you should also use advanced statistical methods like propensity score matching or inverse probability weighting to create the SCA. In doing this, you can adjust all key factors within the trial design to ensure that the synthetic arm closely mirrors what would be expected from a traditional control group.
You might also wish to consider a hybrid trial design where an SCA is used to supplement a smaller traditional control arm rather than the whole thing. This can combine the strengths of both approaches and lead to stronger, more accurate data-driven results.
3. Get regulatory and ethical approval
It’s crucial to get regulatory approval before you start your trial so engage with the relevant regulatory agencies as early as possible to ensure that they accept the use of an SCA in your trial design.
In order to approve your request they may require rigorous validation and transparency in how the SCA is being constructed. They may even want a detailed breakdown of your trial design to support this.
On top of this, you’ll need an ethical review in which an ethics committee or Institutional Review Board (IRB) will also take a look at the use of a SCA. This is done to ensure that the rights and welfare of all patients are preserved.
4. Implement these in your clinical trial and monitor your SCA
The next stage is to integrate this into the trial design and ensure that the trial protocol clearly outlines the construction and validation of the SCA so there are no surprises. It is advisable to test the design ahead of its launch to make sure that there are no unexpected issues, especially in regard to the SCA.
Then, you must continuously monitor the performance of the synthetic control arm throughout the trial to ensure it remains valid and reliable. And at the end, you can analyse the outcomes using the combined data from both the SCA and the active treatment arm.
What are the benefits of using SCAs?
Now we know how to use these methods, let’s address why you should. Synthetic control arms offer several significant benefits in clinical trials, making them an increasingly popular option in modern research. Some of the key reasons to choose this method include:
Increased patient participation: You can achieve higher patient participation as they are often more willing to take part in trials where there is a lower chance of being placed in a placebo group or receiving a less effective treatment.
Ethical advantages: Following on from this, these include the reduced need for placebo groups which is particularly important in life-threatening or serious conditions where withholding treatment could actually be harmful to participants.
Reduction in health and safety risks: Similarly, SCAs are especially useful in reducing patient exposure to potentially ineffective or harmful treatments.
Flexibility and innovation: SCAs allow for the use of hybrid trial designs, combining synthetic controls with smaller traditional control groups. This flexibility can lead to more innovative and adaptive trial designs. Not only that but they can be integrated into adaptive trial designs where the control arm can evolve over time for more responsive studies.
Greater statistical power: SCAs can increase the statistical power of a trial by using a well-matched, robust control group constructed from a larger pool of historical or real-world data.
Reduced bias: The advanced statistical techniques used to construct SCAs help to reduce bias and ensure a fair comparison between the two groups.
Improved rare disease research: SCAs are particularly valuable in researching rare diseases where recruiting enough patients for a traditional control group might be too challenging.
Contributing to personalised medicine: Finally, in personalised medicine where treatments must be tailored to individual patients, SCAs allow for more customised and appropriate control comparisons. This can drastically improve the results of the trial.
