Leaders at Iqvia noted a recent decline in overall patient recruitment for clinical trials across diseases, leading to extended cycle times. After that observation, the company sought ways to address the problem.
David Cameron (DC), Iqvia’s senior director and interim global head for Novel Trial Design, spoke with Outsourcing-Pharma (OSP) about how trials can inconvenience patients and potential recruits. He also addressed how industry stakeholders can alleviate the challenges in different areas of the process.
OSP: What are some of the ways (minor, and major) in which clinical trials place burdens on patient participation?
DC: From the patient perspective, the magnitude of the burden is very subjective and individualized. Examples may be related to the need to schedule time off from work for visits, arrange for childcare, undergo invasive screening procedures, or account for caregiver scheduling. Other types of burdens cited in a 2019 CISCRP report included perceived risk, type and frequency of procedures, travel distance, length of participation, and any incidental unreimbursed costs.
OSP: How has patient burden been addressed in recent years?
DC: One useful trend has been the development of decentralized trials (also called patient-centric or 'virtual' trials) where the trial is brought to the patient via technology and careful planning. Another example involves leveraging data and analytics to evaluate a protocol during the design phase to specifically call out and quantify patient burden in ways that allow for mitigation. However, to-date these efforts have been focused on individual protocols. A more systematic method is needed.
OSP: Are we seeing new burdens pop up to inconvenience patients or discourage participation?
DC: Wearables and devices can provide a tremendous amount of data. However, if there are barriers to the use of the devices by patients, this can cause significant inconvenience to the patient. Such barriers can include complexity in use, a tendency to break, slow 'help desk' response times or limited availability of back up devices. In such instances, patients, coordinators and study teams must spend time fixing or troubleshooting and back up methods of collecting the data must be implemented to allow patients to participate when the use of the device is a challenge for them.
OSP: Could you please explain how protocol design trends have negatively impacted recruitment and retention?
DC: Inclusion and exclusion criteria have become stricter, resulting in smaller available patient pools. This often also requires the inclusion of more investigators with access to patients, as well as perhaps setting up referral networks.
OSP: How does that lead to longer cycle times?
DC: More time is needed to sign up investigators and give them a sufficient recruitment window. Referral networks, when applicable, add an additional layer of complexity, cost and time.
OSP: Could you please tell me more about how a new methodology to pinpoint burdens could transform burden assessment? What would this methodology look like?
DC: Such a methodology:
- Above all, focuses on the patient’s perspective of burden;
- To the extent possible, accounts for real-world variability in individual patient journeys, as opposed to default 'standards of care' or 'national treatment guidelines;'
- Facilitates aggregation and segmentation at a variety of levels (e.g., protocol, indication, geography, treatment type, demography);
- Enables objective comparison;
- Derives practically from available real-world data, as well as patient and clinical trial characteristics;
- Supports decision-making via transparency, replicability and validation;
- Encompasses both patient and caregiver elements; and,
- Translates readily into the patient’s calculus metrics, namely time, cost, and disruption to daily routine.
OSP: How would such a methodology help provide trial designers and managers with greater insight about reducing patient burden?
DC: Such a methodology would enable standardized, personalized comparisons of incremental burden across trials and patient populations. Further, it would facilitate additional research on factors that impact burden, and how burden impacts participation metrics.
OSP: What would it take for trial designers and managers to implement such a transformation?
DC: In addition to the creation of such a metric, one would need to understand how to calculate it and apply it. This likely will require analytic methods tied to real-world data and demographics. Sophisticated machine learning approaches would help refine burden calculations and extrapolate those calculations to other populations.