SPACE Framework (Structured Preapproval and Postapproval Comparative study design framework)
The SPACE Framework helps design studies using real-world data to compare medicines before and after they are approved. It makes the study design process clear and trustworthy by structuring decisions, especially around what factors might bias results, so that regulators and other stakeholders can better understand and trust the findings.
At a glance
Use when
Designing real-world studies to inform regulatory decisions, especially for preapproval and postapproval safety or effectiveness comparisons
Avoid when
When real-world data are of poor quality or insufficient granularity, or when rapid, exploratory analysis without formal validation is needed
Inputs
Research question, real-world data sources, knowledge of clinical context, potential confounders, causal assumptions
Outputs
Structured study design documentation, causal diagram, justified design choices, transparency report for regulatory review
How it works
The Structured Preapproval and Postapproval Comparative study design framework (SPACE) is a systematic method for designing observational studies using real-world data to support regulatory decision-making. It emphasizes transparency and validity by guiding researchers through key design elements, including formulation of a clear research question, identification of validity threats, use of causal diagrams (e.g., DAGs) for confounding control, and documentation of design choices, assumptions, and supporting evidence. The framework draws on principles from randomized controlled trials and pragmatic study design to balance internal validity with real-world relevance.
- HTA domains
- Clinical Effectiveness, Safety, Organisational aspects
- Assumptions
- Real-world data can yield valid causal inferences if study design is rigorous and transparent; causal diagrams help identify necessary confounders; regulatory stakeholders value documented design rationale
- Strengths
- Enhances transparency and reproducibility; integrates causal inference principles; supports regulatory-grade evidence; promotes stakeholder trust through documented decision-making
- Limitations
- Requires expertise in causal inference and study design; may be resource-intensive to implement fully; dependent on quality and availability of real-world data
- Also known as
- SPACE, Structured Preapproval and Postapproval Comparative study design framework
Questions this answers
- › How can real-world evidence be designed to support regulatory decisions with high validity?
- › What study design elements are essential for minimizing bias in comparative real-world studies?
- › How should confounding variables be selected and justified in observational studies?
- › How can transparency in study design improve trust among regulators, clinicians, and patients?
- › What minimal criteria should be met for feasibility and validity in real-world study designs?
- › How can preapproval and postapproval evidence generation be systematically aligned?
References & sources
Similar by meaning
- SPIFD2 (Structured process to identify fit-for-purpose study design and data for regulatory RWE)
- Good Practices for Real-World Data Studies of Treatment Effectiveness
- ISPOR Prospective Observational Studies to Assess Comparative Effectiveness Good Research Practices Task Force Report
- SPIFD (Structured Process to Identify Fit-For-Purpose Data)
- STaRT-RWE
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.