Ryan Bailey, MA is a Senior Clinical Researcher at Rho. He has over 10 years of experience conducting multicenter asthma research studies, including the Inner City Asthma Consortium (ICAC) and the Community Healthcare for Asthma Management and Prevention of Symptoms (CHAMPS) project. Ryan also coordinates Rho’s Center for Applied Data Visualization, which develops novel data visualizations and statistical graphics for use in clinical trials.
In a recent New York Times article, Paula Span raises the concern that elderly subjects are frequently omitted from clinical trials. Consequently, physicians know very little about how a given treatment may affect their older patients. Is a medication effective for the elderly? Is it safe? Without data, how is a physician to know?
Span’s article is timely and aligns well with similar industry trends toward increased patient centricity and trial diversity. Yet, expanding trials to include older patients poses a challenge for research teams because it brings two tenets of quality research into conflict with one another – representative study populations and patient safety.
The fundamental assumption of clinical trials research is that we can take data from a relatively small, representative selection of subjects and generalize the results to the larger patient population. If our sample is too constrained or poorly selected, we hinder the broad applicability of our results. This is not merely a statistical concern, but an ethical one. Unfortunately, our industry has long struggled with underrepresentation of important demographic groups, especially women, racial and ethnic minorities, and the elderly.
At the same time, researchers are keenly concerned about protecting subject safety in trials. Good Clinical Practice is explicit on this point:
2.3 The rights, safety, and well-being of the trial subjects are the most important considerations and should prevail over interests of science and society.
Such guidance has engendered broad reluctance to conduct trials in what we deem “vulnerable populations,” namely children, pregnant, and the elderly. The risk of doing more harm than good in these patient groups often leads us to play it safe and exclude these populations from trials. Span, however, provides an astute counterpoint: expecting providers to prescribe a medication to a group of patients who were not included in the original research is equally irresponsible.
No case illuminates the challenging catch-22 we face like the awful thalidomide debacle of the 1950s-60s. Thalidomide, which was widely regarded as safe, was prescribed off-label for pregnant women to treat morning sickness. Tragically, the drug was later linked to severe birth defects and banned for expecting mothers.
On one hand, the physicians prescribing thalidomide did so based on limited knowledge of the drug’s safety in pregnant women. Had a trial had been conducted that demonstrated the risk to children, they would clearly know not to prescribe it to expecting mothers. Yet, the very risk of such dangerous complications is why such trials are not conducted in vulnerable populations in the first place. Risks for the elderly are different than for pregnant women, but the principal of protecting sensitive populations is the same.
Span notes that even in studies that don’t have an explicit age cap, many protocols effectively bar elderly participants via strict exclusion criteria that prevent participation by people with disorders, disabilities, limited life expectancy, cognitive impairment, or those in nursing homes. It must be stated, however, that the reason for such conditions is not to be obstinately exclusive but to reduce confounding variables and minimize risks to vulnerable patients. In most cases, it would be patently unethical to conduct research on someone with cognitive impairment or in a nursing home where they may be unable to give adequate informed consent, or they may feel coerced to participate in order to continue receiving care.
So, how do we negotiate this apparent impasse? Span offers a few general suggestions for increased inclusion, including restructuring studies and authorizing the FDA to require and incentivize the inclusion of older adults. Changing the laws and enforcement can certainly drive change, but what can we do in the near term, short of legislative intervention?
A few quick suggestions:
- Reconsider age limits and avoid an all-or-none mentality to enrolling geriatric subjects. The mindset that older adults are, as a whole, too vulnerable to enroll is usually an overreach. In most cases, age limits are imposed as a convenience for the study, not a necessity. Instead, consider evaluating eligibility on a subject-by-subject basis, which will still allow exclusion of patients deemed too frail, risky, or comorbid for the trial.
- Actively recruit older subjects. The lack of geriatric patients in our trials is a result of many years of both passively and actively excluding them, so effort is needed to reverse these trends. Beyond recruitment for an individual trial, researchers and providers should seek to educate older adults about clinical research. Many elderly patients may be research-naïve – unfamiliar with clinical trials and how to participate, or unaware of available trials in their area.
- Learn from other efforts to recruit marginalized populations. As we’ve shared previously, improving trial diversity starts with an effort to thoroughly understand your patient population and their needs, and reduce obstacles to their participation.
- Engage patient advocacy groups that focus on elderly patients. Ask how trials can be better designed to meet their needs and include them. Partner with these groups to aid in information sharing and outreach.
- Learn what is already expected from agencies like the FDA and NIH when it comes to inclusivity.
- Span alludes to a recent NIH policy revision (stemming from the 21st Century Cures Act) that will require new NIH grantees to have a plan for including children and older adults in their research.
- In 2012, the Food and Drug Administration Safety and Innovation Act (FDASIA) required the FDA to create an action plan to improve data quality and completeness for demographic subgroups (sex, age, race, and ethnicity) in applications for medical products.
- Design studies to examine effectiveness (demonstrating that a treatment produces desired results in ‘real world’ circumstances) not just efficacy (demonstrating that a treatment produces desired results in ideal conditions). This is probably the most labor intensive because it requires additional investment beyond the typical Phase III randomized controlled clinical trial. Yet, it is becoming more common to explore effectiveness through pragmatic trials, Phase IV studies, and post-market surveillance.