Data Management

Pacing obesity trial enrollment, anchoring eligibility to stable real-world therapies, and building site and physician relationships improve retention and data quality more than aggressive recruitment or restrictive exclusion criteria do.

In this Q&A, Saamir Pasha and Carole Berini, PhD, of Ontada discuss what it takes to harmonize fragmented oncology data into reliable real-world evidence, how lab markers can proxy for missing functional status data, and where the biggest remaining barriers to consistent RWE generation lie.

Webinar Date/Time: Thu, Nov 19, 2026 1:00 PM EST

Real-time clinical trials require standardized digital data capture at the point of care through scaled eSource-EDC-EHR integration, but progress stalls because competing standards lack regulatory enforcement and one-off system integrations cannot scale beyond single studies without industry-wide alignment and legal clarity on data ownership.

Webinar Date/Time: Thursday, November 5, 2026 11:00 AM EST

Real-time RBQM requires proportionate analysis frequency matched to data arrival and issue criticality, not continuous reanalysis, because signal churn from over-frequent runs creates alert fatigue and operational noise that obscures meaningful deterioration.

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

Mixed FSP and FSO models are gaining ground as sponsors seek agility and resource flexibility, but the oversight infrastructure, audit trails, and accountability structures needed to run them well and survive regulatory inspection are consistently lagging behind the model itself.

Hierarchical composite endpoints analyzed through pairwise comparisons more accurately reflect multifaceted treatment benefit than time-to-first-event composites, but transparent outcome prioritization, patient involvement in ranking, and reporting of Net Treatment Benefit remain underutilized despite their importance to interpretation.

In this Q&A, Sylviane de Viron of CluePoints and Abigail Dirks, MS, of Tufts CSDD discuss the findings of a collaborative study quantifying the financial value of RBQM, why time savings emerged as the largest driver, and what sponsors struggling to justify adoption now have that they didn't before.

Sponsor oversight of outsourced CRO work is often robust in practice but fails inspection because oversight decisions are fragmented across systems and lack an audit trail, requiring sponsors to document oversight as a connected operating system with clear decision records, escalation pathways, and issue resolution from start to finish.

In rare disease drug development where no registry or natural history dataset exists, real-world evidence quality depends on treating evidence engineering as a design-stage decision, defining intended regulatory use and standardizing endpoints, harmonization, and data provenance upfront rather than reconciling gaps after collection.

In this episode of Beyond Compliance, Otis Johnson, PhD, MPA, founder and principal consultant at Vantix Operations, speaks with Cecilia Xi, PhD, VP of clinical and scientific affairs at Vivalink, about why defensible trial data depends on traceability, platform ownership, and device strategy long after a study ends.

Decentralized trial models have demonstrated real gains in enrollment performance and patient access, but operational switching costs, fragmented technology stacks, and unresolved gaps in the patient-site relationship raise a question the industry has been slow to confront directly.

Most AI pilots in drug development fail not from poor technology but from lack of strategic prioritization, organizational readiness, integrated data infrastructure, and disciplined governance, making success dependent on business discipline rather than technical capability.

Why clinical development’s next AI bottleneck is not the model, but the system around it.

Regulatory expectations for patient input in drug development have shifted from aspiration to documented methodology across three major jurisdictions.

This episode of The Human Side of Clinical Trials, hosted by Brian S. McGowan, PhD, FACEHP, chief learning officer and co-founder of ArcheMedX, Inc., and Kelly Ritch, chief operating officer of ArcheMedX, Inc., explores why training completion metrics like attendance and quiz scores fail to capture true study readiness, and how measuring both competence and confidence can reveal trial risks before enrollment even begins.

Clinical trials have spent decades perfecting data capture, but the execution layer underneath it, including decision workflows, data collection design, and lab connectivity, remains fragmented in ways that limit what AI can realistically deliver and that quietly compromise the scientific validity of the data itself.

Participant engagement depends more on clarity and understanding than on gamification or incentives, requiring engagement embedded into trial workflows as a core design component rather than a peripheral consideration

In this video interview, Sylviane de Viron of CluePoints and Abigail Dirks of Tufts CSDD discuss what the RBQM financial model left out—reduced rework, improved data integrity, and inspection readiness—and why those excluded factors could represent even greater value than what was modeled.

Biomarker-informed trial enrollment appears straightforward for identifying responders and optimizing efficiency, but failures to account for demographic variation in cut-off selection, causality versus correlation, and composite biomarker complexity risk excluding viable patients and exacerbating disparities.

Agentic AI will put the CRO operating model to the test—and force the efficiency and accountability questions the industry can no longer avoid.