Published on 27/12/2025
How to Evaluate Investigator Experience with Similar Clinical Studies
Introduction: Why Investigator Experience Matters in Feasibility
In the site feasibility phase of clinical trials, one of the most predictive indicators of site performance is the principal investigator’s (PI) experience with studies of comparable design, indication, and complexity. Selecting an investigator with aligned experience increases the probability of timely recruitment, protocol adherence, data quality, and regulatory compliance. Conversely, mismatched or underqualified investigators can lead to delays, protocol deviations, and even regulatory inspection findings.
This article outlines a comprehensive approach for evaluating PI experience based on similar studies, including what metrics to review, documentation requirements, scoring methodologies, and real-world examples of best practices.
1. Defining “Similar Studies” in the Context of PI Evaluation
“Similar studies” are typically defined by matching one or more of the following attributes:
- Therapeutic area: e.g., oncology, endocrinology, neurology
- Study phase: Phase I (intensive PK/PD), Phase III (large multicenter), etc.
- Patient population: e.g., pediatric, geriatric, rare disease
- Study design: e.g., double-blind, crossover, adaptive design
- Procedural intensity: e.g., number of biopsies, imaging requirements
Experience with trials of matching design and complexity ensures the investigator understands not just the medical aspect but also the logistical and regulatory framework.
2. Core Documents for Experience
During feasibility, sponsors and CROs typically collect the following to assess PI experience:
- Updated CV (within 2 years)
- Investigator Site File (ISF) experience logs or summary tables
- Feasibility questionnaire responses
- GCP training certificate (valid and dated)
- Records of previous sponsor studies (study name, year, phase)
- Enrollment metrics (actual vs. target)
Pro tip: Ask for the last 3–5 studies by therapeutic area, phase, and outcome to enable performance comparison.
3. Scoring Model for PI Experience Alignment
Sponsors often apply a weighted scoring model to rank PI experience. A sample model might include:
| Experience Domain | Weight | Scoring Criteria |
|---|---|---|
| Therapeutic Area Experience | 30% | None (0), Moderate (1), High (2) |
| Protocol Complexity Experience | 25% | No match (0), Partial (1), Full match (2) |
| Recruitment Performance in Prior Studies | 20% | <50% (0), 50–100% (1), >100% (2) |
| Compliance Record (Protocol Deviations) | 15% | High (0), Low (2) |
| Audit/Inspection History | 10% | Negative (0), None (1), Positive (2) |
Investigators with scores above a defined threshold (e.g., 80%) proceed to selection or pre-study visits.
4. Qualitative Insights Beyond the Scorecard
In addition to numeric scoring, sponsor feasibility teams gather qualitative insights such as:
- Investigator’s leadership style and site staff feedback
- Experience working with the same CRO or sponsor
- Willingness to adapt to decentralized or digital trial formats
- Responsiveness and communication during the feasibility process
- Past involvement in protocol design or advisory boards
These soft factors often predict investigator engagement and retention across lengthy or complex protocols.
5. Red Flags in PI Experience Review
During CV or questionnaire review, feasibility managers should watch for red flags such as:
- No history of sponsor-conducted trials
- Outdated GCP certification (>3 years old)
- Exaggerated experience claims (e.g., listing trials never published or registered)
- History of protocol violations or IRB complaints
- Refusal to provide full past performance data
Investigators exhibiting multiple red flags should be deprioritized or required to submit remediation plans.
6. Case Study: Experience Mismatch Impact
Scenario: A respiratory disease trial selected a PI whose past experience was in endocrinology. Though the site had strong infrastructure, the PI underestimated spirometry calibration needs, leading to multiple protocol deviations and a partial clinical hold.
Outcome: The sponsor revised their feasibility SOPs to include phase and indication alignment checks and introduced a standardized experience scoring tool.
7. Using Investigator Databases and Networks
Feasibility teams may also use established PI databases or networks to validate or identify investigators with relevant experience:
- TransCelerate’s Investigator Registry
- CDISC/CDER site performance datasets
- Historical data from internal CTMS systems
- ClinicalTrials.gov or WHO ICTRP trial participation history
These tools help triangulate CV data and validate experience claims.
8. Templates for Investigator Experience Summary
For internal documentation and regulatory readiness, it’s helpful to summarize PI experience in a standard template. A sample format includes:
| Study | Phase | Therapeutic Area | Enrollment Target | Actual Enrollment | Start–End Date |
|---|---|---|---|---|---|
| ABC123 | III | Cardiology | 50 | 55 | Jan 2021 – Dec 2021 |
| XYZ045 | II | Respiratory | 40 | 35 | Mar 2020 – Nov 2020 |
This structure simplifies review and supports regulatory inspections or IRB queries.
Conclusion
Evaluating a PI’s experience with similar studies is one of the most powerful predictors of site success. Sponsors and CROs must adopt a structured, data-driven approach to assess past experience, using aligned metrics, scoring systems, and real-world performance indicators. When matched correctly, experienced investigators drive recruitment, maintain compliance, and deliver reliable data—making them essential assets to any clinical development program.