Incorporating Real-World Data into Phase 2 Study Planning: A Practical Guide

Introduction

As clinical development evolves, the use of Real-World Data (RWD) is expanding beyond post-marketing surveillance into earlier phases of research. In Phase 2, RWD can enhance trial design, support site selection, inform endpoint development, and even help construct external comparator arms. This tutorial explores how RWD can be effectively integrated into Phase 2 study planning to increase efficiency, reduce costs, and enhance relevance to real-world settings.

What Is Real-World Data?

Real-World Data (RWD) is health-related data collected outside traditional randomized controlled trials (RCTs). Common sources include:

• Electronic Health Records (EHRs)
• Insurance claims and billing data
• Patient registries and disease databases
• Digital health tools, wearables, and apps
• Home-based patient monitoring and telehealth platforms

Benefits of RWD in Phase 2 Study Planning

• Improves Feasibility Assessments: Estimates eligible population size and site potential
• Informs Endpoint Selection: Identifies clinically relevant outcomes from real-world practice
• Refines Inclusion/Exclusion Criteria: Avoids overly restrictive protocols
• Supports External Control Arm Development: Especially for rare diseases and oncology
• Enhances Recruitment Strategies: Identifies high-volume centers and patient demographics

Where RWD Fits in Phase 2 Planning

• Pre-Protocol Design: Feasibility assessments, population characteristics, endpoint relevance
• Protocol Finalization: External control arm definitions, real-world comparators
• Operational Planning: Site selection, patient identification, stratification logic

Case Example: RWD Use in Oncology Phase 2 Trial

A biotech company planning a single-arm Phase 2 study for a rare cancer used real-world claims and registry data to:

• Define median progression-free survival in standard-of-care cohorts
• Justify historical control benchmarks to the FDA
• Select sites with the highest concentration of biomarker-positive patients

Applications of RWD in Key Design Decisions

1. Inclusion/Exclusion Criteria

• Review EHR data to understand comorbidity burden in target population
• Simulate how protocol criteria would affect eligibility rates

2. Endpoint Validation

• Use RWD to assess how often events (e.g., hospitalization, exacerbation) occur in practice
• Choose endpoints that are both clinically relevant and measurable

3. External Comparators

• Construct historical controls using propensity score matching from registry or EHR data
• Useful in oncology, rare diseases, or when placebo is unethical

4. Trial Site Selection

• Use hospital and claims data to identify high-volume providers
• Assess enrollment likelihood based on regional demographics

Best Practices for Integrating RWD

• Engage real-world data experts early during planning
• Define RWD objectives clearly—feasibility vs. comparator vs. endpoint validation
• Ensure data quality, completeness, and standardization
• Document all methodologies, assumptions, and limitations
• Address ethical and privacy concerns, especially in international studies

Regulatory Viewpoints on RWD in Phase 2

FDA (U.S.)

• Supports RWD in early development through the Real-World Evidence (RWE) Framework
• Accepts external comparators when well justified and transparent

EMA (Europe)

• Encourages RWD for feasibility, endpoint selection, and safety surveillance
• Recommends early dialogue with regulatory authorities

CDSCO (India)

• Limited guidance, but accepts registry-based control arms in oncology and rare diseases

Tools and Platforms for RWD Access

• TriNetX, IBM Explorys, Flatiron Health, Optum Labs
• SEER-Medicare, OMOP Common Data Model databases
• Global registries (e.g., NORD, EurORDIS, disease foundations)

Challenges and How to Overcome Them

• Data Variability: Standardize definitions and coding systems
• Missing Data: Use statistical methods (e.g., imputation) or supplement with chart reviews
• Selection Bias: Apply robust matching or weighting approaches
• Privacy Concerns: Use de-identified or aggregated data and obtain IRB approval

Conclusion

Real-world data can significantly enhance Phase 2 study planning by grounding trial designs in practical, population-based evidence. From refining eligibility criteria and selecting meaningful endpoints to identifying trial sites and constructing external comparators, RWD is becoming an essential part of modern clinical development. By using high-quality data and following ethical, statistical, and regulatory best practices, sponsors can build smarter, more efficient, and more impactful Phase 2 trials.