DOI: 10.25934/pr00008997
title: The integration of clinical trial data, real-world evidence, and novel statistical methods to optimise control arm recruitment in relapsed and refractory multiple myeloma

description: Multiple myeloma (MM) is a type of blood cancer which affects the way white blood cells make antibodies. It’s the second most common blood cancer, and accounts for 1% of all cancers and about 10% of all blood cancers. Compared to people without MM, patients with multiple myeloma are only 55% as likely to survive for at least 5 years. There is no known cure, however new treatments developed in recent years have allowed patients to live longer. Unfortunately, most patients’ disease will eventually stop responding to treatment. These patients will have to move on to other treatments, but available treatments for these patients are still limited. 

Randomised-controlled trials (RCTs) are research studies used to determine how effective a new treatment is in comparison to a different, more established treatment. In an RCT, patients are randomly assigned (like flipping a coin) to either receive the new treatment or a control treatment, which could be a proven treatment or placebo, i.e. something that looks like medicine but has no medicinal effects. This helps researchers compare the benefits and side-effects between the two. Sometimes it’s not possible to do this kind of study; for example, if a new treatment seems to be highly effective and safe from preliminary studies, if would be unethical to not provide it to those eligible to receive it. In these cases, researchers will instead use existing data from previous studies or electronic health records to create a control group. This approach allows the new treatment to be compared to a control treatment, but the type of people receiving the new treatment and control may be different, so the comparison may not be as accurate as in an RCT. In this study, we will use electronic health records (EHR) to create an External Control Arm (ECA) and compare it to the existing control group in the ICARIA-MM trial. An ECA can either replace or add to an existing control group and is useful for proving how well the new treatment works. However, finding good data to create ECAs can be challenging, especially when it comes to rare or uncommon diseases. A consequence of this is that there still uncertainty as to how ECAs should be developed when trying to provide a comparison for a trial, and how ECA data should be analysed alongside trial data.  

The primary goal of this study will be to determine scenarios in which construction of an ECA can lead to accurate assessment of the relative effectiveness of new treatments when compared to treatments that are currently in use. The aims of this study will be achieved by constructing an ECA dataset using de-identified routine electronic health data for over 6700 MM patients available through the Arcturis Data Platform. This dataset will allow us to choose patients who could have been included in the ICARIA-MM trial and evaluate common trial outcomes such as how long a patient survives after receiving treatment. This mimics an RCT without the additional burden on patients and associated recruitment costs of an actual control group.  

We will compare the benefits of the new treatment in the ICARIA-MM study against the ECA. We will show differences in survival between the two groups using Kaplan-Meier curves. To account for any differences between the patients in both groups, we will use statistical techniques like inverse probability weighting that adjust the data based on each individual patient's characteristics. Additionally, we will use a method called Bayesian borrowing to investigate how we should select patients for the ECA based on the characteristics of the original control group of the trial.