The Relationship Between Investigator Experience and Enrollment Performance
Published October 2025 — Sites led by principal investigators with more than 10 years of clinical research experience enrolled at 1.4x the rate of less experienced investigators, but the gap narrows significantly when sites have dedicated sub-investigators and strong coordinator teams.
Beyond Individual Experience: The Team Effect
Principal investigator (PI) experience has long been considered a primary indicator of site enrollment potential. Sponsors and CROs routinely prioritize PIs with extensive clinical research track records during site selection, and investigator databases rank experience — measured in years of practice, number of prior studies, and therapeutic area familiarity — as a top-tier selection criterion.
This emphasis on PI experience is not unfounded. Our data confirm that sites led by PIs with more than 10 years of clinical research experience enrolled at 1.4 times the rate of sites led by PIs with fewer than 5 years of experience. However, the more important and less recognized finding is that this experience gap narrows dramatically — to just 1.08x — when less experienced PIs are supported by dedicated sub-investigators and well-structured coordinator teams.
This research brief presents a comprehensive analysis of the relationship between investigator experience, team composition, and enrollment performance across 44 multi-site clinical trials and 198 participating sites. The findings challenge the industry’s disproportionate focus on PI credentials and make a data-driven case for evaluating site teams holistically — a shift that has significant implications for site selection, site development, and workforce planning across the clinical research enterprise.
Key Findings
The analysis encompasses 198 sites across 44 studies, with detailed data on PI experience, team composition, and enrollment outcomes for over 8,400 enrolled patients.
Sites led by PIs with more than 10 years of clinical research experience enrolled patients at 1.4 times the rate of sites led by PIs with fewer than 5 years of experience, measured as patients enrolled per site per month across the enrollment window.
When sites led by less experienced PIs had dedicated sub-investigators (at least 1 per study) and coordinator-to-study ratios below 3:1, the enrollment rate gap narrowed from 1.4x to just 1.08x — a difference that was not statistically significant (p = 0.31).
The strongest single predictor of enrollment velocity was not PI experience but coordinator-to-study ratio. Sites with a coordinator-to-study ratio of 2:1 or better enrolled at 2.6 times the rate of sites where coordinators managed 5 or more studies simultaneously.
Sites with optimized team structures (dedicated sub-investigator, coordinator-to-study ratio below 3:1, and regular team meetings) had 38% lower protocol deviation rates regardless of PI experience level, suggesting that team quality is more predictive of data quality than individual investigator experience.
Methodology
The analysis used a retrospective cohort design with prospective data collection on team composition variables not typically captured in standard feasibility or site performance databases.
Investigator Experience Classification
PIs were classified into three experience tiers based on years of active clinical research involvement (not total years of medical practice): Tier 1 comprised PIs with more than 10 years of clinical research experience (n=76 sites); Tier 2 comprised PIs with 5-10 years of experience (n=72 sites); and Tier 3 comprised PIs with fewer than 5 years of experience (n=50 sites). Experience was validated against investigator database records, publication histories, and sponsor-reported trial participation data. Therapeutic area-specific experience was recorded separately: a PI with 15 years of general clinical research experience but only 2 years in oncology was classified as Tier 1 overall but Tier 3 for oncology-specific experience.
Team Composition Assessment
For each of the 198 sites, a detailed team composition survey was administered to the site research director or lead coordinator, capturing the number and experience level of sub-investigators assigned to the study, the coordinator-to-study ratio (total active studies divided by full-time equivalent research coordinators), the presence or absence of a dedicated regulatory specialist, the presence or absence of a dedicated patient recruitment specialist or navigator, and the frequency and structure of team meetings (daily huddles, weekly reviews, ad hoc only). Team composition was assessed at two time points — study initiation and mid-enrollment — to capture staffing changes that occurred during the trial.
Outcome Measures and Statistical Approach
The primary outcome was enrollment velocity (patients enrolled per site per month). Secondary outcomes included time to first patient enrolled (from site initiation visit), screen failure rate, protocol deviation rate per patient-visit, data query rate per CRF page, patient retention rate at 6 and 12 months, and a composite site performance score integrating enrollment, quality, and retention metrics. Multivariate regression models were constructed to estimate the independent effect of PI experience while controlling for team composition, therapeutic area, trial phase, site type (academic vs. community), and geographic region. Interaction terms between PI experience and team composition variables were included to test the hypothesis that team quality moderates the PI experience effect.
The Team Composition Effect
The most significant finding of this analysis is that team composition variables collectively explain more of the variance in enrollment performance than PI experience alone. Three team factors emerged as the strongest predictors.
The coordinator-to-study ratio was the single strongest predictor of enrollment velocity across the entire dataset, outperforming PI experience, site type, and therapeutic area familiarity. Sites where coordinators managed 2 or fewer studies simultaneously achieved a median enrollment velocity of 2.8 patients per month, compared to 1.9 patients per month at sites with 3-4 studies per coordinator and just 1.1 patients per month at sites with 5 or more studies per coordinator. The relationship was nonlinear: the enrollment penalty for each additional study per coordinator was modest up to 3 studies but accelerated sharply beyond that threshold. At 5+ studies, coordinators reported spending less than 30% of their time on any single study, leading to delayed patient follow-up, missed screening windows, and slower data entry that compounds into enrollment underperformance.
Sites with at least one sub-investigator dedicated to the study (not shared across 4+ concurrent studies) enrolled at 1.6x the rate of sites where the PI was the sole physician investigator. Sub-investigators provide critical operational capacity: they can conduct screening assessments, review safety data, manage adverse events, and perform study visits when the PI is unavailable — which, at busy academic and community practices, can be 40-60% of clinic hours. The sub-investigator effect was additive with the coordinator effect: sites with both a dedicated sub-investigator and a coordinator-to-study ratio below 3:1 enrolled at 2.3x the rate of sites with neither, regardless of PI experience tier.
Sites that held regular, structured team meetings — defined as at least weekly meetings with a documented agenda covering enrollment status, screening pipeline, and operational issues — enrolled at 1.3x the rate of sites that relied on ad hoc communication. The mechanism is straightforward: regular meetings ensure that screening bottlenecks are identified within days rather than weeks, that coordinator workload is balanced across team members, and that the PI remains informed about enrollment progress and can intervene with referring physicians when referral volume declines. Sites where the PI personally attended weekly enrollment meetings (rather than delegating to a coordinator) showed a 14% higher enrollment velocity than sites where PI engagement was limited to monthly reviews.
The Role of Sub-Investigators in Enrollment Performance
The sub-investigator role is frequently undervalued in site selection processes, which tend to focus almost exclusively on the PI’s credentials. Our analysis reveals that the presence and quality of sub-investigators is a critical determinant of both enrollment velocity and data quality.
Among the 198 sites in the dataset, 124 (63%) had at least one sub-investigator assigned to the study. Of these, 68 (55%) had sub-investigators who were dedicated primarily to the study (assigned to no more than 2 concurrent studies), while 56 (45%) had sub-investigators who were shared across 3 or more studies. The enrollment velocity benefit of sub-investigators was concentrated almost entirely in the dedicated sub-investigator group: sites with dedicated sub-investigators enrolled at 1.6x the rate of sites without sub-investigators, while sites with shared sub-investigators enrolled at only 1.15x the rate — a difference that was not statistically significant.
The quality impact of sub-investigators was equally notable. Sites with dedicated sub-investigators had 29% lower protocol deviation rates, 22% fewer data queries per CRF page, and 16% faster query resolution times compared to sites without sub-investigators. These quality improvements were attributable to the sub-investigator’s ability to provide real-time medical oversight during study visits, review source documentation before data entry, and serve as a second set of eyes for eligibility confirmation — functions that a PI managing multiple clinical and non-clinical responsibilities cannot consistently provide.
An important finding for workforce development is the experience pathway from sub-investigator to PI. Among the 50 Tier 3 PIs (fewer than 5 years of experience) in the dataset, 34 (68%) had previously served as sub-investigators. These former sub-investigators achieved enrollment velocities 18% higher than Tier 3 PIs without prior sub-investigator experience, suggesting that the sub-investigator role serves as an effective clinical research training ground that accelerates the development of enrollment-ready investigators.
Mentorship Models and Investigator Development
The analysis identified three mentorship models in use across the network, each with distinct outcomes for investigator development and enrollment performance.
Formal Mentorship Programs
Twelve sites in the dataset operated formal mentorship programs where experienced PIs were paired with newer investigators for a structured development period spanning 2-3 studies. Mentees in these programs achieved Tier 2-equivalent enrollment performance by their second study — approximately 18 months earlier than investigators without formal mentorship. The mentorship programs covered four domains: patient identification and screening strategy, regulatory and compliance management, team leadership and delegation, and sponsor relationship management. The cost of formal mentorship was estimated at $15,000-$25,000 per mentee (primarily in senior PI time allocation), which was recovered within 12 months through the mentee's improved enrollment contribution.
Informal Peer Networks
Twenty-eight sites participated in informal peer networks where investigators at similar experience levels shared enrollment strategies, discussed protocol challenges, and benchmarked their performance against peers. These networks were facilitated by the site organization or CRO but did not involve formal mentor-mentee relationships. Investigators in peer networks showed a 12% higher enrollment velocity than isolated investigators at the same experience tier, attributable to the rapid sharing of practical enrollment tactics — such as referral source optimization, pre-screening workflows, and patient communication strategies — that would otherwise take years to develop through individual experience.
Sponsor-Supported Investigator Training
In 8 of the 44 studies, sponsors provided enhanced investigator training beyond the standard investigator meeting, including site-specific enrollment coaching, therapeutic area deep dives, and ongoing enrollment consultation throughout the study. Sites receiving sponsor-supported training showed a 21% higher enrollment velocity in the first 90 days of enrollment — the period when the knowledge gap between experienced and inexperienced investigators is most impactful. The benefit diminished over time as all investigators gained study-specific experience, suggesting that targeted early-enrollment support is the most efficient use of sponsor training investment.
Site Staffing Optimization Framework
Based on the multivariate analysis, we identify the optimal team configuration for maximizing enrollment performance across different study complexity levels. The framework defines three staffing models calibrated to study demands.
Standard Complexity Studies
For studies with 12 or fewer eligibility criteria, 6 or fewer study visits, and standard safety monitoring requirements, the optimal team configuration is: one PI (any experience tier), one coordinator dedicated to no more than 3 studies, and a sub-investigator shared across no more than 2 studies. This configuration achieved a median enrollment velocity of 2.4 patients per month with a protocol deviation rate of 2.8% per patient-visit. Sites meeting this staffing standard performed equivalently regardless of PI experience tier, confirming that adequate team support normalizes the PI experience effect for routine studies.
High Complexity Studies
For studies with more than 12 eligibility criteria, complex dosing regimens, extensive safety monitoring (DSMB-overseen, with frequent SAE reporting), or patient populations requiring specialized medical management, the optimal configuration adds a dedicated sub-investigator (assigned to no more than 1-2 studies), a coordinator-to-study ratio of 2:1 or better, and a dedicated regulatory specialist. In this configuration, PI experience becomes more relevant: Tier 1 PIs outperformed Tier 3 PIs by 1.25x even with full team support, reflecting the medical judgment and protocol interpretation skills that complex studies demand and that can only be developed through extensive experience.
High-Enrollment-Target Studies
For studies with per-site enrollment targets exceeding 15 patients, the staffing model must scale beyond the standard configuration regardless of study complexity. The data show that enrollment velocity plateaus at approximately 3.2 patients per month with a single coordinator, regardless of how few other studies that coordinator manages. Sites that exceeded this velocity consistently had 2 or more coordinators assigned, with responsibilities split between screening/enrollment activities and ongoing patient management/data entry for already-enrolled subjects. This split-responsibility model produced 40% higher sustained enrollment velocities than models where a single coordinator managed both functions, because it eliminates the common pattern of enrollment slowdowns during periods of high data entry burden from previously enrolled patients.
Conclusions
The clinical trial industry’s emphasis on principal investigator experience as the primary predictor of site enrollment performance is understandable but incomplete. While PI experience matters — and matters more for complex studies — the data demonstrate convincingly that team composition is a stronger and more modifiable determinant of enrollment velocity, data quality, and patient retention.
The practical implication is a shift in how the industry evaluates and selects sites. Rather than ranking sites primarily by PI credentials, sponsors and CROs should assess the complete research team: the coordinator-to-study ratio, the presence and dedication level of sub-investigators, the frequency and structure of team communication, and the availability of specialized support roles. Sites that score well on these team composition metrics will consistently outperform sites with prestigious PIs but thin operational support.
For the clinical research workforce, these findings reinforce the importance of investing in coordinator capacity, sub-investigator development, and structured mentorship programs. The 1.4x enrollment rate advantage of experienced PIs is real but can be substantially replicated — and in some cases exceeded — by building well-structured teams around less experienced investigators. In an era of increasing trial volume and a finite pool of experienced investigators, the ability to develop effective research teams is not just an operational advantage — it is a strategic necessity for the sustainable growth of clinical research capacity.
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