SPIFD2 (Structured process to identify fit-for-purpose study design and data for regulatory RWE)
SPIFD2 is a structured method that helps researchers choose the right study design and real-world data for regulatory decisions. It ensures that studies are valid, transparent, and fit for purpose by guiding users to clearly define their target trial, assess potential biases, and document their choices step by step.
At a glance
Use when
Developing real-world evidence for regulatory submissions, designing observational studies for pre- or post-approval decision making, or justifying data and design choices to regulators
Avoid when
When randomized controlled trials are feasible and sufficient; when data quality is too poor to support any credible emulation of a target trial; or when there is insufficient expertise to apply the framework rigorously
Inputs
Research question, regulatory context, candidate study designs, potential real-world data sources, knowledge of bias domains in observational studies
Outputs
Documented, justified selection of fit-for-purpose study design and data sources, including target trial emulation and bias assessment, aligned with STaRT-RWE reporting standards
How it works
SPIFD2 integrates and updates the 2019 SPACE framework and the 2021 SPIFD method into a unified process for generating regulatory-grade real-world evidence. It emphasizes the articulation of a hypothetical target trial, identification of biases in real-world study emulation, and integration with the STaRT-RWE reporting tool. The framework provides a systematic, documented approach to justify study design and data source selections, enhancing validity, transparency, and reproducibility for regulatory use.
- HTA domains
- Clinical Effectiveness, Safety, Organisational aspects
- Assumptions
- Real-world evidence can meet regulatory standards if study design and data selection are rigorously justified; transparency and documentation improve trustworthiness and decision-making
- Strengths
- Enhances validity and transparency of real-world evidence; supports regulatory acceptance; integrates with structured reporting tools; promotes reproducibility and clear communication with stakeholders
- Limitations
- Requires significant expertise in study design and real-world data; may be resource-intensive; dependent on data availability and quality; not a substitute for randomized trials when they are feasible
- Also known as
- SPIFD2, Structured Process to Identify Fit-for-Purpose Study Design and Data, SPACE-SPIFD2 integrated framework
Questions this answers
- › What study design is best suited to answer a specific regulatory question using real-world data?
- › Which real-world data sources are fit for purpose given the research question and target trial emulation?
- › How can sources of bias in real-world evidence studies be systematically identified and addressed?
- › How can transparency and reproducibility be ensured in real-world study design and data selection?
- › How should the rationale for design and data choices be documented for regulatory review?
- › What tools support structured reporting after applying the SPIFD2 framework?
References & sources
Similar by meaning
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.