A Data-Driven Framework for Clinical Trial Site Selection
Published November 2025 — A structured, evidence-based approach to site identification and qualification that balances quantitative performance metrics with qualitative operational readiness indicators.
The Site Selection Challenge
Site selection is arguably the single most consequential operational decision in clinical trial execution. The sites chosen to participate in a study determine enrollment velocity, data quality, patient diversity, and regulatory compliance — yet the process by which sites are selected remains largely subjective in much of the industry.
Industry benchmarks reveal that approximately 11% of activated sites fail to enroll a single patient, while another 37% significantly under-enroll relative to their commitments. The financial impact of poor site selection extends well beyond wasted activation costs — it includes extended timelines, protocol amendments driven by enrollment shortfalls, and the opportunity cost of delayed market entry.
This paper presents a comprehensive, data-driven site selection framework developed through Clinitiative’s experience managing multi-site clinical trials across diverse therapeutic areas. The framework integrates quantitative performance scoring with qualitative operational assessment to produce site portfolios that are optimized for enrollment probability, operational reliability, and patient population access.
The Problem with Traditional Site Selection
Conventional site selection processes suffer from several systematic weaknesses that undermine trial performance.
Over-Reliance on Investigator Relationships
Site selection decisions frequently prioritize established relationships between sponsors or CROs and principal investigators. While investigator experience is valuable, it is an insufficient basis for selection when not paired with objective performance data. A principal investigator with 20 years of experience and strong publications may lead a site that is operationally under-resourced, over-committed to competing studies, or located in a region with low disease prevalence for the target indication.
Feasibility Questionnaire Bias
The standard feasibility questionnaire asks sites to self-report their patient access, enrollment capacity, and operational readiness. Research has repeatedly demonstrated that investigator enrollment estimates are systematically overestimated — by an average of 45% across therapeutic areas. Sites have a natural incentive to project optimistic enrollment numbers, and feasibility questionnaires provide no mechanism for independent validation of these claims.
Geographic Clustering
Without systematic geographic analysis, site portfolios frequently cluster in regions that are convenient or familiar to the study team rather than regions that maximize patient access. This leads to competitive overlap where multiple sites in the same metro area compete for the same patient population, reducing per-site enrollment rates and extending timelines.
Ignoring Operational Readiness Signals
Many site selection processes focus narrowly on patient access and investigator credentials while overlooking operational factors that directly determine enrollment performance. Startup velocity, regulatory review timelines, staff turnover rates, and technology infrastructure maturity are all strong predictors of site performance — yet they are rarely systematically assessed during feasibility.
The Clinitiative Site Selection Framework
Our framework evaluates potential sites across five dimensions, each weighted according to its demonstrated correlation with enrollment success and data quality outcomes.
Patient Accessibility Index (Weight: 30%)
The Patient Accessibility Index quantifies the concentration of eligible patients within a site’s referral network and geographic catchment area. It integrates disease prevalence data, insurance coverage profiles, demographic composition, and referral network mapping to estimate the realistic addressable patient population. Unlike self-reported estimates, this index uses external data sources — including de-identified claims data, disease registries, and census-level demographic information — to independently validate patient access claims.
A site located in a metropolitan area with 2 million residents may have a lower Patient Accessibility Index than a site in a mid-sized city with 400,000 residents if the larger city has lower disease prevalence, higher competition from other active trials, or insurance coverage patterns that exclude the target population from clinical trial participation.
Historical Performance Score (Weight: 25%)
Historical performance is assessed not as a single aggregate metric but as a multi-dimensional profile that includes enrollment rate relative to commitment (actual vs. projected), screen failure rate relative to therapeutic area benchmarks, data query rates and resolution timelines, protocol deviation frequency and severity classification, and time from site activation to first patient enrolled. Each metric is normalized against therapeutic area and trial phase benchmarks, ensuring that a site’s performance in a rare disease Phase II trial is not unfairly compared to performance in a large cardiovascular Phase III program.
Operational Readiness Assessment (Weight: 20%)
Operational readiness evaluates a site’s current capacity and infrastructure to execute a new study. This assessment examines current study load and coordinator availability, technology infrastructure including EDC experience and eSource capability, institutional review board or ethics committee review timelines, and regulatory document readiness and contract negotiation velocity. Sites are categorized into three readiness tiers — Immediate, Near-Term, and Development — based on how quickly they can realistically initiate enrollment following study award.
Competitive Landscape Analysis (Weight: 15%)
The competitive landscape analysis maps all known active and planned clinical trials in the same therapeutic area and patient population within each site’s geographic region. This analysis identifies sites where patient competition is minimal and enrollment probability is highest, sites that are over-saturated with competing studies and likely to under-enroll, and strategic opportunities to activate sites in underserved trial markets where patient demand exceeds supply of clinical trial options.
Diversity and Representation Index (Weight: 10%)
Regulatory agencies and sponsors increasingly require clinical trial populations that reflect the demographics of the intended treatment population. The Diversity and Representation Index evaluates each site’s ability to enroll a demographically representative patient cohort based on the catchment area demographic composition, historical enrollment demographics from prior studies, and community engagement capabilities and relationships with underrepresented populations. This dimension ensures that site portfolios are not only optimized for speed and efficiency but also for the demographic representativeness that regulators and patients expect.
Validation Results
The framework was retrospectively validated against 85 completed multi-site trials and prospectively applied to 18 active studies.
The proportion of activated sites that failed to enroll a single patient dropped from 11% to 3% when the framework was applied prospectively, representing a 73% reduction in zero-enrolling sites.
Trials using the framework completed enrollment an average of 34% faster than comparable trials using conventional site selection methods, measured from first site activated to last patient enrolled.
Optimized site portfolios required 28% fewer sites to achieve the same enrollment targets, reducing site management overhead and improving per-site enrollment rates.
Studies that incorporated the Diversity and Representation Index achieved a 41% improvement in enrollment of underrepresented populations compared to therapeutic area averages.
Implementation Considerations
Transitioning from relationship-based to data-driven site selection is not simply a technology upgrade — it requires organizational commitment to evidence-based decision-making. Sponsors must be willing to challenge the assumption that familiar sites are the best sites. Clinical operations teams must be empowered to recommend against sites that score poorly on objective criteria, even when those sites have strong investigator relationships.
The framework is designed to augment rather than replace clinical judgment. Quantitative scoring identifies the strongest candidates from a large universe of potential sites; qualitative assessment by experienced clinical operations professionals validates and refines the final portfolio. The combination of data-driven identification and human-validated selection produces site portfolios that are both statistically optimized and operationally sound.
Conclusions
The evidence is clear: data-driven site selection produces measurably better outcomes than traditional approaches across every dimension that matters — enrollment speed, data quality, cost efficiency, and patient diversity. The framework presented in this paper provides a structured, reproducible methodology that can be applied across therapeutic areas, trial phases, and geographic regions.
As clinical trials become more complex and competition for patients intensifies, the margin for error in site selection continues to shrink. Organizations that adopt rigorous, data-driven site selection today will establish a structural advantage in trial execution efficiency that compounds across their development portfolio.
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