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Diversity in clinical trial participation is critical for scientific validity, social good, and regulatory considerations. Unfortunately, populations that are historically and intentionally disinvested by healthcare systems are also often underrepresented in clinical trials. Lack of diversity in clinical trial participant populations can lead to:
- Inaccurate predictions of interventions' real-world effects
- Failure to understand how different populations might respond differently to interventions
- Missed opportunities to design and tune interventions for in-need persons
The risks and opportunities are particularly relevant for digital health in general and digital therapeutics (DTx) specifically, where value is often predicated on how digital platforms may be less bound by existing infrastructure and geography.
Unfortunately, digital health solutions often run into the same issues with trial recruitment as traditional medical trials. We can mitigate some of these risks by rethinking the way we approach clinical trial evaluation and design, allowing us to better meet the needs of our patient populations and fulfill regulatory compliance measures.
Social risk stratification: A use case
How can Company X address diversity and inclusion in its clinical trial participants?
Let's take the case of Company X, a relatively new digital therapeutics organization that is trying to evaluate and diversify its clinical trial participant population. The organization has run successful trials to date on their products and are working on running more late-phase clinical trials soon.
Company X wants to know:
- How well do our existing trials recruit a diverse and inclusive population?
- What can we learn about how different populations respond differently to our interventions from tour existing trials?
- How can future trials be better designed to incorporate and analyze how social characteristics interacts with the effects of our interventions?
- How does the value of the product change with respect to reaching different populations in terms of cost-effectiveness?
Challenges Company X might face while evaluating clinical trial diversity
Company X is likely to face three key challenges while pursuing answers to the four objectives:
- Identifying social determinants of health in the participant population
- Recruiting participants
- Trial design
- Cost-effectiveness of the model
More about these clinical trial diversity challenges:
Identifying social determinants of health in the population of interest
Diversity, including, and social determinants of health, are multi-factorial concepts that require a deep dive into understanding the composition of the patient populations with respect to the intervention. While single factors such as race and education are often used as proxy measurements of social risk, diversity and inclusion requires synthesizing a large array of data and qualitative assessments of the populations of interest. Often these data are simply not available at an individual level.
Traditionally, trials often recruit from major medical centers based in research institutions. That tends to lead to trial participants being drawn from highly served communities whose demographics are unrepresentative of the patient population of interest. Because these communities have been historically and often currently marginalized from the medical community, community representation in underserved populations is particularly important.
Designing clinical trials for diversity
Clinical trials must be designed with specific intent to draw from a diverse population. If this requirement isn't established in the earliest phases in conjunction with other parameters, it is likely the trial will ultimately draw from relatively homogenous populations. It will then lack sufficient statistical power necessary to accommodate examination of differences between social groups among the participants.
To meet these objectives, health solutions providers most likely need to engage in substantially more complex sampling and corresponding analysis designs than may be more typical in a standard two-group comparison. Recruiting from historically and intentionally disinvested populations often requires alternative strategies, community representation, and sampling designs. Each of these tactics has implications on the way the statistical analysis is performed and interpreted to yield robust, valid, and useful results.
Interpretability and credibility of cost-effectiveness
Cost-effectiveness analysis always requires making assumptions and dealing with uncertainty. That is even more true for more complex trial designs with historically disinvested and less commonly studied populations. The more assumptions and uncertainties required, the less reliable the model. In digital health, where one of the primary value prospects is the relatively low cost of delivery, establishing credible cost-effectiveness analysis can be challenging.
Solutions offered by RTI Health Advance
Addressing the issues surrounding diversity in clinical trials in the digital health space requires a comprehensive and interdisciplinary approach at both macro and micro levels. Here is how RTI Health Advance might work with the Company X to solve these issues and meet its objectives.
Stage 1: Assess existing trial participant populations
As a first pass, geographic assessment of the patient populations can tell you quite a bit about the social risks of the patient populations, even when little individual data are available.
RTI Health Advance uses RTI Rarity™ to comprehensively examine a wide array of Census tract-level social risk factors to give an overall first look at the likely social needs of the existing participant population. This yields a rapid assessment of the population, as well as a line of sight into how trials might be targeted in the future.
Once that step is completed, RTI Health Advance can take a deeper dive into the population characteristics both at the individual and geographic levels. If possible, trial results can be stratified by social needs and social risk factors to examine differences in effects in different population groups. In instances where a trial is not specifically designed for this type of stratification, insights may be unavailable or inconclusive. Members of our team will likely be able to assess methodological changes necessary for future designs, however.
Stage 2: Future trial and cost-effectiveness analysis design
If findings indicate that current trials were not sufficiently able to include and analyze groups in diverse populations, RTI Health Advance's team can advise on and support trial design and analysis efforts. This can include:
- Expertise on recruitment
- Targeting populations using RTI Rarity and other tools
- Simulation-based statistical power assessments of different designs
- Analysis plans matching the design
- Built-in cost-effectiveness analysis to minimize assumptions needed
The complexity and interconnectedness of the issues revolving around historically and intentionally excluded patient populations requires designing them collectively to assess tradeoffs and generate creative and novel solutions. RTI Health Advance's interdisciplinary team and experience can be a strategic resource for analyzing both existing data and designing robust evidence in the future.
Build diversity in clinical trials from the ground up
To meet the needs of current and future health evidence, we need to rethink how we engage and include participant populations at every stage. While there are some gains to be made re-analyzing existing data retroactively, diversity and inclusion in trials can only be achieved by building it in from the ground up, at every level of clinical trial design.
RTI Health Advance helps digital health companies stratify data according to social risk, determining if goals for reaching populations with increased social needs are met. Contact us to schedule a consultation.
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