Designing Feasibility Questionnaires Using Historical Site Data

The Importance of Historical Site Data in Feasibility Planning

Feasibility questionnaires are foundational tools in clinical trial planning. They help sponsors and CROs identify and select high-performing sites based on several factors like patient pool, investigator experience, infrastructure, and regulatory track record. However, when these questionnaires are designed without historical context, they can result in overly optimistic or inaccurate site responses. That’s where leveraging historical site data becomes critical.

Historical site data includes past enrollment rates, protocol deviation frequencies, screen failure rates, regulatory inspection outcomes, and adherence to visit schedules. Sponsors that fail to incorporate this data often face recruitment delays, budget overruns, and poor site compliance. Regulatory bodies including the FDA, EMA, and MHRA emphasize the use of evidence-based feasibility strategies during sponsor inspections.

In this article, we explore how to use historical site data to design smarter, more predictive feasibility questionnaires that improve site selection and study startup efficiency.

Types of Historical Data Relevant to Questionnaire Design

Historical site data spans multiple domains. The most useful categories include:

• Enrollment History: Number of subjects enrolled in similar trials within a specific timeframe
• Protocol Adherence: Frequency of deviations and their root causes
• Screen Failure Rates: Percentage of screened patients not meeting inclusion criteria
• Site Activation Timelines: Average time from contract finalization to first patient in (FPI)
• Regulatory Inspection Outcomes: FDA 483 observations, MHRA findings, or internal QA audits

Below is an example data summary from three sites in a cardiovascular trial:

From this table, it’s evident that Site A outperformed others in all key areas. Integrating this insight into a questionnaire helps to focus future feasibility assessments on parameters that matter.

Integrating Data into Feasibility Questionnaire Logic

Feasibility tools often consist of static checklists or self-reported site capabilities. When these are integrated with historical performance data, they become much more predictive. Here’s how historical data can enhance questionnaire sections:

• Recruitment Potential Section: Pre-fill enrollment numbers from past studies and ask the site to explain any changes
• Protocol Adherence Section: Highlight deviation patterns from previous trials and assess current mitigation measures
• Timeline Commitments: Use actual past activation data to validate new timeline estimates

For example, a dynamic form might display: “In your last three trials in this therapeutic area, your average enrollment was 20 patients over 6 months. What has changed to support your estimate of 60 patients in this protocol?”

This approach discourages over-promising and helps differentiate high-performing, realistic sites from aspirational responders.

Sources of Historical Site Data

Historical site data can be gathered from several internal and public sources:

• Clinical Trial Management Systems (CTMS): Capture site-level metrics from previous studies
• Electronic Data Capture (EDC) Platforms: Document protocol adherence and visit windows
• Trial Registries: Data from Be Part of Research (NIHR) and other registries to validate enrollment timelines
• Quality Management Systems (QMS): Archive audit outcomes, CAPA timelines, and deviations

Sponsors that maintain a structured site master file with past feasibility, audit reports, and performance summaries can extract this data with minimal effort. It’s also beneficial to include CRO partner databases and publicly available performance scores (e.g., from the TransCelerate Shared Investigator Platform).

Feasibility Questionnaire Elements That Benefit from Data Integration

Not all parts of a feasibility questionnaire require historical data, but certain sections benefit significantly from it:

By designing forms with auto-filled historical fields, sponsors can reduce bias and increase transparency. Some tools even allow scoring systems based on prior performance benchmarks.

Case Study: Data-Driven Feasibility Yields Better Enrollment

In a 2023 Phase II neurology study, the sponsor used historical site performance data to filter out low-recruiting sites from a previous epilepsy trial. By incorporating metrics such as “patients enrolled per FTE” and “visit adherence rate,” they excluded 30% of sites that had previously delayed timelines. The remaining sites achieved 95% of the recruitment target three months ahead of schedule.

This outcome illustrates how applying historical metrics during feasibility tool design directly impacts enrollment, cost, and data integrity.

Tools and Platforms That Support Data-Driven Questionnaire Design

Sponsors can use various platforms to operationalize this approach:

• CTMS Platforms: Veeva Vault CTMS, Medidata RAVE
• Feasibility Tools: SiteIQ, Clinscape Feasibility Module
• Analytics Dashboards: Tableau, Power BI connected to CTMS/EDC sources
• Risk-Based Monitoring Tools: RBM dashboards that include performance trend lines

These systems allow sponsors to design adaptive questionnaires, conduct real-time validation of site claims, and score site responses against benchmarks.

Challenges and Considerations

Despite the advantages, there are challenges to using historical data:

• Data inconsistency across CROs and systems
• Lack of access to complete legacy data for global sites
• Privacy and data protection regulations (e.g., GDPR)
• Misinterpretation of context (e.g., poor performance due to protocol flaws, not site issues)

Therefore, sponsors must contextualize historical data and allow sites to provide explanations for deviations or poor performance. Data should be used to initiate dialogue, not penalize sites without cause.

Conclusion

Designing feasibility questionnaires using historical site data enables evidence-based site selection, reduces trial risk, and improves regulatory compliance. Sponsors should move away from static, self-reported surveys and adopt dynamic, data-informed tools that consider past performance. Platforms such as CTMS, QMS, and analytics dashboards can help integrate these insights into feasibility tools, creating a predictive framework for identifying high-performing, inspection-ready sites. In doing so, the industry takes a meaningful step toward smarter, faster, and more reliable clinical trial execution.

Essential Feasibility Metrics for Selecting the Right Clinical Trial Sites

Choosing the right investigational sites is one of the most critical factors influencing the success of a clinical trial. Site feasibility assessments go beyond basic questionnaires—they require evaluating robust metrics that predict a site’s ability to deliver high-quality data, recruit effectively, and meet regulatory standards. This guide explores key feasibility metrics sponsors and CROs should use to select optimal clinical trial sites.

Why Metrics Matter in Site Feasibility

Traditional site selection methods often rely on subjective impressions or past relationships. However, with rising regulatory expectations and protocol complexity, data-driven site selection is now essential. Metrics offer:

• Quantifiable insight into site capabilities
• Better forecasting for patient enrollment
• Improved operational planning
• Reduced risk of non-compliance or delays

Resources such as StabilityStudies.in offer best practices for site documentation and trial integrity.

Top Feasibility Metrics to Evaluate Trial Sites

1. Historical Patient Recruitment Performance

• Number of patients enrolled in previous trials in the same indication
• Speed of enrollment compared to target timelines
• Drop-out and screen failure rates

2. Study Start-Up Timelines

• Average time for Ethics Committee (EC) approval
• Contract finalization time with the sponsor/CRO
• Site initiation visit (SIV) readiness time

3. Regulatory and Audit History

• Number of audits in the last 5 years
• Findings and CAPA responses, if applicable
• Compliance with GMP audit checklist and ICH-GCP standards

4. Therapeutic Area Experience

• Number of trials conducted in the relevant indication
• Specific expertise of principal investigator (PI)
• Availability of trained sub-investigators and coordinators

5. Site Infrastructure Readiness

• Availability of diagnostic tools, labs, and investigational pharmacies
• Functionality of EDC systems and internet bandwidth
• Facilities for IP storage, sample shipment, and patient comfort

Scoring and Ranking Feasibility Metrics

To effectively use metrics, develop a scoring matrix that assigns weights to each criterion based on study priorities. For example:

• Patient Recruitment History: 35%
• Startup Timelines: 25%
• PI and Staff Experience: 15%
• Infrastructure Readiness: 15%
• Audit/Compliance History: 10%

Sites are scored and ranked. Sites below a threshold may be excluded or flagged for risk mitigation.

Digital Tools to Track and Analyze Metrics

• Clinical Trial Management Systems (CTMS)
• Feasibility dashboards within eTMF platforms
• Excel feasibility scoring templates
• CRA report-based feasibility validations

These tools help gather and compare site data across global networks efficiently.

Integrating KPIs into Site Selection SOPs

Use internal Pharma SOP guidelines to standardize feasibility evaluations across studies. SOPs should define:

• What data should be requested
• How metrics are scored and interpreted
• Who is responsible for final site approval

Having consistent feasibility practices improves quality and regulatory inspection readiness.

Regulatory Expectations and Documentation

According to USFDA and EMA, site selection must be justified with documented feasibility assessments. Sponsors must ensure that the process is auditable and that decisions are supported by objective data.

Challenges and Mitigation Strategies

• Incomplete Data from Sites: Encourage sites to provide performance metrics in feasibility questionnaires.
• Overestimated Recruitment: Cross-check against therapeutic benchmarks or past enrollment logs.
• Resource Constraints: Consider central site services or additional monitoring resources.

Conclusion

Feasibility metrics offer a strategic advantage in selecting high-performing clinical trial sites. By using a structured, metrics-driven approach to feasibility, sponsors can reduce risk, optimize enrollment, and ensure quality and compliance throughout the study lifecycle. Effective site selection starts with objective data, not guesswork.