Complexities With Conducting Real-World Dementia Trials


The director of the Institute for Health, Health Care Policy, and Aging Research at Rutgers University discussed the issues that remain with enrolling patients into real-world dementia trials and standardizing measures.

XinQi Dong, MD, MPH

XinQi Dong, MD, MPH

This is a 2-part interview. To view part 1, click here.

At the request of the National Institute on Aging (NIA), the National Academies of Sciences, Engineering, and Medicine recently convened an ad hoc expert committee to assist the NIA and the broader dementia community. In a corresponding published report, they assessed the evidence on care interventions for persons living with dementia and their caregivers, provided information on decision-making about which interventions should be broadly disseminated and implemented, and guidance for future actions and research by laying out a blueprint for success.

The group notes in the report that although some dementia interventions have been tested in large randomized clinical trials, many have only been tested in academic settings with fewer participants that do not fully represent the overall diverse population of those living with dementia. The conclusions made do not call into question fundamental aspects of high-quality dementia care, services, and supports, but rather suggest a need for additional research on specific interventions.

XinQi Dong, MD, MPH, director of the Institute of Health, Health Care Policy, and Aging Research, Rutgers University, was among those involved with the report and claims that real-world studies are necessary but come with challenges. In the second half of an interview with NeurologyLive, Dong gives an inside perspective on why these real-world dementia trials struggle to find their legs. He also provided thoughts on the need to standardize consistent measures used across these trials.

NeurologyLive: What are the barriers and challenges that are associated with conducting real-world dementia studies?

XinQi Dong, MD, MPH: I think there’s strongly a trust issue in the community, especially unrepresented and underserved populations. The history of trust and mistrusting research runs deep in our communities. There are many examples of that throughout the recent decades in history. That’s an important barrier from both a community and participants’ level. As researchers, I don’t think we do as much outreach as we ought to sometimes to be able to communicate the clear and concise messages to why someone should enroll in research projects.

There are also methodological issues in terms of feasibility and readiness of community to be able to enroll in research studies. There are also linguistic and cultural issues. We can’t simply translate a cognitive measure for one population to another without understanding the implications of that. Does it actually measure what we intended to measure? Oftentimes translations need to be validated and tested to make sure they’re reliable. Those are the steps that often get short circuited. When we gather data, we’re not sure what that means.

There’s really multiple levels of barriers in terms of making sure that inclusiveness is not just the count of how many underrepresented minorities are enrolled in the studies, but rather, the study methodology.

What efforts can clinicians take to standardize the measures used in dementia clinical trials?

That’s a great question that unfortunately I don’t have the full answer for. But I can tell you a couple of areas which we’ve been working on. One, which is getting normative data for specific populations, such as New Jersey, which is incredibly diverse. Understanding what is considered normal cognitive data in different populations is really important. The second thing is understanding much more in-depth and nuanced the culture, meanings, and issues related to dementia and Alzheimer disease-related dementia. Those things are really important because if we can’t have accuracy in what we want to measure, then there’s no way we’re going to understand how to interpret the data. It goes back to what I said earlier, we can’t just simply translate a document and think that’s going to do the trick. We have to understand, what are the norms? Until we know what the normal ranges are, we can’t make concrete diagnosis on what is considered abnormal.

Transcript edited for clarity.

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