Cynthia Qi, Health Economics Outcomes Researcher at Argenx, discusses myasthenia gravis and real-life data.
Transcription:
Hello, everyone. My name is Cynthia Qi. I am a Health Economics Outcomes Researcher at Argenx. I’ve been working in this field for more than 10 years. The majority of work I do is basically running research to understand the healthcare outcomes or the healthcare economics of treatments, of patients’ particular disease. Basically, it’s driven by data, and we try to use the most scientific methodology to find an answer.
I joined Argenx, for more than two years, I’ve been studying the myasthenia gravis condition and related outcomes since then. I wouldn’t say I’m a full expert compared to a lot of clinicians who have practiced a lot, but I have definitely learned a lot about different data sources about the patient’s burden, about the outcomes, about economic elements of this particular condition.
I would first describe the general challenges about real-world data and then add on to that the challenges about rare disease. Real-world data, the benefit of that is usually you’re going to see a much larger sample. It will capture a much broader population. But then the downside is you will be limited by what type of elements you’re available. Sometimes you may also be limited by how the data is gathered, the mechanism that data is collected.
There needs to be some interpretations or assumptions adopted or certain methodology that we apply to make the most sense out of the data. That’s a general constraint for any research of real-world data.
But to add on to rare disease, the challenges we see there are usually we’re further limited by sample size. Depending on the nature of the disease, we may also limit it by the type of data elements that is more related. Like cardiovascular or diabetes, you can easily find large samples of patients where you can power your study. But for rare diseases, we’re naturally limited by the number of patient cases we can identify in the real world. Then compounded by that, we may not always have the most relevant outcomes measures, or the type of outcomes that patient cares about, or clinicians cares about, is available.
That’s some of the general challenges and areas to take into consideration when interpreting the analysis that we’re about to discuss.
To learn more about myasthenia gravis and other rare neurological disorders, click here: https://checkrare.com/diseases/neurology-nervous-system-diseases/