EDC for Real-World Evidence (RWE) Studies

EDC for real-world evidence studies: RWD collection, eConsent, ePRO, and 21 CFR Part 11–aligned controls for regulatory and HTA use.

21 CFR Part 11–aligned · HIPAA-aligned security · No credit card required

Meeting regulatory expectations for RWE data quality

The regulatory landscape for real-world evidence has matured significantly. The FDA’s Real-World Evidence Program (established under the 21st Century Cures Act), the EMA’s guidances on real-world data, and similar initiatives from regulatory authorities worldwide have established clear expectations for RWE data quality. Central to these expectations is the concept of "fitness for purpose"—data must be collected and managed with quality controls appropriate for the intended regulatory use. For prospective RWE studies, fitness for purpose typically requires: a pre-specified protocol and analysis plan, informed consent documentation, structured data capture with audit trails, quality control processes (edit checks, query management), and data governance that ensures only authorized personnel can access and modify study data. These requirements closely parallel those for interventional trials, and they necessitate an EDC system with the same compliance infrastructure. Capture provides this infrastructure as standard. Every data entry is logged with timestamp, user identity, and reason for change. Electronic signatures are 21 CFR Part 11–aligned. Role-based access ensures appropriate data governance. And structured exports provide the data provenance documentation that regulatory reviewers need to assess data reliability.

  • Data capture aligned with FDA Real-World Evidence Program and EMA real-world data guidance expectations
  • Pre-specified protocol support with structured CRFs, edit checks, and quality control workflows
  • Complete audit trails documenting data provenance from entry through analysis
  • Electronic signatures and role-based access meeting 21 CFR Part 11–aligned standards
  • Structured exports supporting regulatory reviewer assessment of data reliability and fitness for purpose

Combining prospective and retrospective data in RWE studies

Many RWE studies use hybrid designs that combine prospective data collection with retrospective data abstraction. For example, a study might retrospectively abstract baseline characteristics and treatment history from medical records while prospectively collecting outcome data, patient-reported assessments, and follow-up information. This hybrid approach leverages the efficiency of existing data while ensuring that critical endpoint data is captured with the quality controls of a prospective study. Capture supports hybrid RWE designs within a single study build. CRFs for retrospective data abstraction can be configured alongside prospective visit-based forms and ePRO assessments. The audit trail distinguishes between data entered from source documents (retrospective) and data collected in real time (prospective), providing transparency about data provenance that is essential for regulatory review. Consent workflows can accommodate the different requirements for prospective participation (active informed consent) and retrospective data use (waiver of consent or broad consent, as approved by the ethics committee).

  • Retrospective data abstraction CRFs and prospective visit-based forms in one study build
  • Audit trail distinguishes between retrospective (source document) and prospective (real-time) data entry
  • Consent workflows accommodate both active informed consent and retrospective data use waivers
  • ePRO can be layered onto retrospective baseline data to capture prospective patient-reported outcomes
  • Unified dataset for analysis eliminates the need to reconcile data from separate systems

RWE for HTA and market access: evidence that payers and HTA bodies accept

Beyond regulatory applications, RWE is increasingly important for health technology assessment (HTA) submissions and market access strategies. HTA bodies such as NICE (UK), G-BA/IQWiG (Germany), HAS (France), and CADTH (Canada) evaluate real-world evidence as part of their assessment of clinical and cost-effectiveness. Payers use RWE to inform formulary decisions, coverage policies, and value-based agreements. For RWE to be accepted in these contexts, the data must meet standards for methodological rigor and data quality. This includes transparent data collection methods, pre-specified analysis plans, quality-controlled data capture, and documentation that supports reproducibility. An EDC system with audit trails, structured data capture, and compliant export formats provides the foundation for generating RWE that HTA bodies and payers will consider credible. Capture supports RWE programs designed for HTA and market access by providing the data governance infrastructure these stakeholders expect. Study teams can demonstrate that data was captured with appropriate quality controls, that the audit trail is complete, and that exports are reproducible—building confidence in the evidence among the diverse audience of decision-makers who will evaluate it.

  • Data governance infrastructure aligned with HTA body expectations (NICE, G-BA, HAS, CADTH)
  • Structured data capture with pre-specified CRFs, edit checks, and quality control processes
  • Audit trails that support transparency and reproducibility requirements for HTA submissions
  • Export formats compatible with health economic modeling and cost-effectiveness analysis tools
  • Documentation supporting data quality claims in HTA dossiers and payer submissions

Overview

EDC for Real-World Evidence (RWE) Studies

Real-world evidence (RWE) studies generate evidence from real-world data (RWD) collected outside the controlled environment of traditional clinical trials. RWE is used to support regulatory decisions (label extensions, post-marketing commitments, safety evaluations), health technology assessment (HTA) submissions, payer negotiations, and clinical practice guideline development. The FDA, EMA, and other regulatory authorities have published frameworks and guidances establishing how RWE can complement traditional clinical trial data, and the use of RWE in regulatory and reimbursement decisions is growing rapidly. For RWE studies to achieve their purpose, the underlying data must be fit for purpose: captured with appropriate quality controls, traceable through audit trails, and managed with data governance standards that can withstand regulatory scrutiny. This is where many RWE programs encounter challenges. Data collected in spreadsheets, generic databases, or systems without audit trails may not meet the evidentiary standards required by regulators and HTA bodies, regardless of how well the study was designed. Your EDC must support prospective, retrospective, or hybrid data capture with consent, optional ePRO, and full auditability so data can support submissions and publications. The system should accommodate the diverse data sources common in RWE: clinician-entered CRFs, patient-reported outcomes, data abstracted from electronic health records, and patient-generated health data from wearables or mobile devices. Capture provides EDC, eConsent, ePRO, and safety in one platform with 21 CFR Part 11–aligned controls and HIPAA-aligned infrastructure. The platform supports RWE study teams in collecting and managing real-world data with the data integrity and traceability that regulatory agencies and HTA bodies expect. Because all data flows into one system with one audit trail, teams can demonstrate data provenance from collection through analysis—a critical requirement for RWE that will be used in decision-making contexts.

Why RWE studies need the right EDC

RWE studies differ from traditional clinical trials in several ways that affect EDC requirements. Visit schedules may be driven by clinical care rather than protocol-defined windows. Data may come from multiple sources (clinician assessment, patient self-report, record abstraction) within the same study. Consent requirements may vary by jurisdiction and data source. And the evidence generated must meet standards for regulatory or HTA use that go beyond what is expected for internal analyses. Legacy EDC systems designed for interventional trials often impose structures—fixed randomization modules, mandatory treatment assignment fields, rigid visit windows—that create friction in RWE study operations. At the same time, lightweight tools (spreadsheets, REDCap, survey platforms) may lack the audit trails, electronic signatures, and access controls needed for data that will be submitted to regulatory agencies or included in HTA dossiers. The gap between what is needed and what is available is particularly acute for prospective RWE studies, where data is collected specifically to generate evidence. These studies require the same level of data governance as interventional trials—complete audit trails, signed consent records, role-based access—but with the operational flexibility to accommodate real-world care patterns. Capture is built for this use case: flexible form and visit design with 21 CFR Part 11–aligned controls and HIPAA-aligned security. The platform supports RWD collection from multiple sources within one system, with one audit trail that provides the traceability regulatory agencies require.

Common RWE study workflow

  • Evidence generation plan: define the research question, target population, data sources, and endpoints
  • Protocol development with statistical analysis plan appropriate for observational/RWE methodology
  • Ethics committee / IRB approval; data protection impact assessment when required
  • EDC build in Capture: CRFs for clinical data, exposure/treatment variables, outcomes, and optional ePRO
  • Consent form configuration for prospective participants; waiver documentation for retrospective components
  • Site activation and coordinator training; decentralized enrollment setup when applicable
  • Participant enrollment: screening, electronic informed consent, eligibility verification
  • Prospective data collection at clinical care visits or protocol-defined intervals
  • Retrospective data abstraction from electronic health records or medical charts into structured CRFs
  • Patient-reported outcome collection via ePRO for quality-of-life, treatment satisfaction, and symptom data
  • Ongoing data review, quality checks, and query resolution by the study team
  • Database lock, data export for statistical analysis, and preparation of regulatory or HTA submissions

Traditional tool pain points

  • EDC built for traditional interventional trials with rigid structures that do not fit RWE flexibility
  • Separate ePRO and EDC systems with no single audit trail, complicating data provenance for regulators
  • High cost and long implementation timelines that exceed RWE study budgets and timelines
  • Limited support for decentralized enrollment and patient-reported data collection in real-world settings
  • Data captured in non-compliant systems (spreadsheets, generic databases) that may not meet regulatory evidentiary standards
  • Inflexible consent workflows that cannot accommodate jurisdiction-specific requirements or waiver documentation
  • Lack of structured export options that align with regulatory and HTA submission formats

How Capture supports RWE studies

  • Flexible EDC with visit-driven, event-driven, or unscheduled data collection to match real-world care patterns
  • Unified platform with integrated eConsent, ePRO, and CRF data—one audit trail for all data sources
  • Consent workflows supporting prospective consent, re-consent for study changes, and waiver documentation for retrospective data
  • 21 CFR Part 11–aligned controls (electronic signatures, audit trails, role-based access) and HIPAA-aligned security
  • ePRO for patient-reported outcomes, treatment satisfaction, and quality-of-life instruments on participant devices
  • Support for both prospective data collection and retrospective data abstraction within the same study
  • Built-in query management and data quality checks aligned with RWE data governance expectations
  • Export for statistical analysis, regulatory submissions, and HTA dossiers in structured formats
  • Documentation for sponsor validation when data will support regulatory or HTA decision-making (Enterprise)
  • Multi-site support with role-based access per site and centralized data review for multi-center RWE programs

FAQ

Questions we hear a lot

Is Capture suitable for real-world evidence studies?
Yes. Capture supports prospective, retrospective, and hybrid RWE study designs with flexible EDC, integrated eConsent and ePRO, and 21 CFR Part 11–aligned controls. The platform provides the data governance infrastructure that regulatory agencies and HTA bodies expect for RWE.
Can data from Capture support regulatory submissions?
Data captured in Capture includes complete audit trails, electronic consent documentation, and structured exports that align with FDA and EMA real-world evidence frameworks. For studies requiring formal validation, the Enterprise tier provides documentation to support sponsor qualification.
Does Capture support HTA and market access RWE programs?
Yes. Capture provides the data governance, audit trail, and export capabilities that HTA bodies and payers expect when evaluating real-world evidence. Study teams can demonstrate data quality, transparency, and reproducibility.
Can we combine prospective and retrospective data?
Yes. Capture supports hybrid RWE designs with retrospective data abstraction CRFs and prospective visit-based forms in one study build. The audit trail distinguishes data provenance (source document vs. real-time entry), and consent workflows accommodate both active consent and retrospective data waivers.
How does consent work for RWE studies?
Capture includes integrated eConsent with electronic signatures, version control, and site-specific management. For prospective participants, active informed consent is captured electronically. For retrospective components, the platform supports documentation of consent waivers as approved by the ethics committee.
Can we include patient-reported outcomes in RWE studies?
Yes. ePRO is integrated within Capture. You can configure quality-of-life instruments, treatment satisfaction surveys, symptom assessments, and other PRO measures. Participants complete them on their own devices, and responses flow into the unified study dataset.
What about data from multiple sources?
Capture supports data from multiple sources within one study: clinician-entered CRFs, patient-reported ePRO, and data abstracted from medical records. All sources feed into one dataset with one audit trail, simplifying analysis and data provenance documentation.
How does Capture handle decentralized RWE enrollment?
Capture supports both site-based and decentralized enrollment. Participants can consent remotely, complete ePRO on their own devices, and have clinical data entered by site staff or through decentralized workflows. The platform accommodates the flexible enrollment patterns common in real-world settings.
What export formats are available for RWE analysis?
Capture exports data in structured formats compatible with R, SAS, Stata, Python, and health economic modeling tools. Exports include complete variable definitions and can be configured for regulatory submissions, HTA dossiers, or publication-ready analysis datasets.
Is Capture compliant for RWE data governance?
Capture is designed with 21 CFR Part 11–aligned controls and HIPAA-aligned security as standard features. These controls provide the data governance foundation that regulatory agencies and HTA bodies require for RWE: audit trails, access controls, electronic signatures, and data integrity controls.
Can we run multi-site RWE studies?
Yes. Capture supports multi-site RWE programs with role-based access per site, centralized data review, and site-specific consent versions. The same platform handles single-site and multi-site RWE studies.
How quickly can we set up an RWE study?
Setup time depends on study complexity. Many RWE studies with straightforward designs deploy within weeks using browser-based configuration. More complex programs with multiple data sources, hybrid designs, or validation requirements may take longer but still benefit from the configuration-driven approach.

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