Behavior Modeling Can Help Sponsors Better Understand Sites And Patients

By Jean Van Rampelbergh, clinical network consultant

Randomized clinical trials are the gold standard in drug development, but their growing complexity drives up cost, while high dropout rates and missing data add considerable risk to endpoint evaluation. Sponsors devote enormous attention to statistical rigor and regulatory compliance. Yet, in my experience, trial success often depends on two perspectives that are too easily overlooked: those of the trial site and the patient.

When protocols are feasible for sites and meaningful for patients, quality improves. When they are not, recruitment, retention, and data integrity suffer. To design trials that can withstand the challenges of the next decade, sponsors must rebalance their focus, bringing the human and operational dimensions to the center of clinical trial design. By identifying patients at risk of dropout and sites that may need additional support, sponsors can build a more resilient research network — one that recruits and retains participants more effectively.

The Site Perspective: Frontline Of Quality

Trial locations are where protocols meet reality. Even the most scientifically robust designs will falter if investigators and staff cannot operationalize them. In practice, feasibility is everything.

I’ve seen investigators and nurses serve as the first salespeople of a study. Their ability to explain the protocol clearly and with confidence directly shapes whether patients enroll. If research team staff are fearful, unconvinced, uninformed, or overwhelmed by the burden of procedures, those barriers transfer to patients.

Too often, protocols load unnecessary procedures onto clinical centers, underestimate the time required, or rely on overly complex schedules. The result is predictable: recruitment slows, retention drops, and quality suffers.

To avoid these consequences, sponsors should engage clinical research locations early, minimize protocol burden, and equip investigators with clear tools and training. More recently, the use of predictive behavioral insights has emerged as an additional way to understand trial center needs — helping identify where extra follow-up, training, or coordinator support could have the greatest impact. In my experience, such behavioral modeling tools enable teams to move from reactive “Why aren’t you enrolling?” conversations to proactive, tailored support for each research center.

Patient Perspective: Beyond Buzzwords

Patient centricity has become a familiar phrase in our industry, but in many protocols, it remains little more than a box-checking exercise.

For potential participants, joining a clinical trial is not routine. It is an added burden on top of their daily lives. Participation requires time, discomfort, effort, and trust. Patients often face long visits, invasive procedures, and unfamiliar terminology. Without meaningful education and support, it is easy to see why some disengage before the study concludes.

True patient centricity means meeting patients where they are. That includes tailoring educational materials by age, condition, and literacy level; designing them to motivate rather than overwhelm; and explaining why participation matters even when outcomes are uncertain. I’ve found behavioral modeling tools to be helpful because they identify participants who are more likely to struggle with visit schedules, complex instructions, or motivation. This allows sponsors to target education and check-ins where they will matter most. When patients feel informed and valued, they are more likely to remain engaged until the last visit.

The Placebo Arm: A Behavioral Challenge

One of the most frequent questions patients ask is, “Why do we still need a placebo?” From a regulatory standpoint, the answer is clear: in a lot of situations, placebos remain essential for determining efficacy. But from a participant engagement perspective, the answer requires more nuance.

Patients often want certainty that they are receiving the active treatment. If they believe a placebo means wasted time, disengagement follows. Sponsors and sites must therefore explain the real value of placebo participation: closer follow-up and education, more attentive disease management, and contribution to scientific progress that benefits the broader patient community.

Trials need training and talking points to convey this perspective. Behavioral insights, such as expectations, motivation, or perceived burden, can guide how coordinators discuss randomization, set expectations early, and schedule extra touchpoints for those most likely to disengage. Using predictive signals helps teams turn a difficult discussion into an informed and supportive one.

Where Technology Fits

Technology alone will not future-proof trial design, but when applied thoughtfully, it can help empower both sites and patients.

Predictive behavioral modeling, for instance, can help identify patients at higher risk of dropout. With these insights, research sites can tailor education and follow-up more precisely, preventing issues before they escalate. The same models can profile research locations, revealing where additional resources or training are needed. Instead of reactive conversations about why enrollment lags, proactive steps can be taken to strengthen recruitment and retention. Over time, repeated use builds a smarter, more confident network of clinical sites that can enroll participants more effectively and adapt to challenges with greater resilience.

Importantly, technology must be integrated with simplicity in mind. Questionnaires, portals, and dashboards add value only if they demonstrate a clear return, fewer dropouts, less missing data, and fewer protocol amendments, rather than adding administrative burden. In my experience, success comes when behavioral tools fit naturally into existing workflows and give coordinators something concrete to act on in real time.

Balancing Complexity And Value

As trials evolve, adaptive designs and decentralized models promise efficiencies. But there is a risk of over-compression: in the name of streamlining, we may inadvertently create complexity elsewhere.

The challenge is to adopt innovations that lighten the load, rather than shifting burdens from one place to another. Every additional requirement, be it a questionnaire or digital platform, should be justified by clear benefits in retention, data quality, or regulatory compliance — and behavioral evidence can help make those decisions.

Looking Ahead

Future-proofing trial design is not about satisfying every regulatory checkbox or just adding technology. It starts with recognizing that sites and patients anchor trial quality. For study locations, feasibility and confidence must come first. Protocols should be realistic to operationalize, supported by training and resources that help investigators and staff present the study with conviction. For patients, education and engagement are critical. Participation must be explained as valuable in every arm, with materials tailored to real people’s needs and daily lives.

These human perspectives remain central, but they are also where trials often falter. That is why innovations like predictive behavioral modeling are so important. By providing evidence-based insights into how sites and patients are likely to respond, these tools give sponsors a way to proactively design for feasibility, motivation, and retention.

About The Author:

Jean van Rampelbergh holds a Ph.D. in biomedical sciences from Université Libre de Bruxelles. His expertise spans all clinical development activities. Throughout his career he’s held clinical research leadership roles at Sanofi and Imcyse, and is currently collaborating with INNODIA, an international non-profit organization working to develop new cures and therapies for people living with type 1 diabetes.