SPIFD (Structured Process to Identify Fit-For-Purpose Data)
SPIFD is a step-by-step method to assess whether real-world data sources are suitable for making reliable healthcare decisions. It helps researchers find high-quality data that fits the needs of a specific study, especially when supporting regulatory decisions.
At a glance
Use when
Designing real-world evidence studies for regulatory submissions, evaluating data sources for pharmacoepidemiology research, ensuring transparency in data selection
Avoid when
Working with highly novel or emerging data types without established evaluation criteria, non-regulatory exploratory research where data rigor is less critical
Inputs
Research question, list of potential real-world data sources, study objectives, regulatory context
Outputs
Assessment of data source suitability, documented justification for data selection, transparency report on data feasibility
How it works
SPIFD provides a systematic framework for conducting feasibility assessments of existing real-world data sources to determine their fitness for purpose in supporting regulatory and clinical decision-making. It builds on the SPACE framework and aligns with FDA's real-world evidence program, guiding users from research question formulation to data source evaluation and study design justification.
- HTA domains
- Clinical Effectiveness, Safety, Organisational aspects
- Assumptions
- Data sources can be systematically evaluated for quality and relevance; user has access to metadata or data profiles; alignment with regulatory standards enhances credibility
- Strengths
- Enhances transparency and reproducibility; supports regulatory-grade evidence generation; integrates with established frameworks like SPACE and FDA RWE guidelines
- Limitations
- Requires detailed knowledge of data sources; may not be applicable to all types of health technology assessments; dependent on availability and accessibility of real-world data
- Also known as
- Structured Process to Identify Fit-For-Purpose Data
Questions this answers
- › How do I know if a real-world data source is suitable for my study?
- › What criteria should I use to assess data quality and relevance?
- › How can I ensure transparency and justification in data selection?
- › How does this data support regulatory decision-making?
- › What steps are needed to evaluate data feasibility systematically?
- › How can I align my data assessment with FDA real-world evidence expectations?
References & sources
Similar by meaning
- SPIFD2 (Structured process to identify fit-for-purpose study design and data for regulatory RWE)
- Framework for Assessment of the Use of FAIR Principles for Health Data in Digital Health Devices Regulation
- SPACE Framework (Structured Preapproval and Postapproval Comparative study design framework)
- Data-driven FMEA approach for hazard identification and risk evaluation in digital health
- IDEAL-D Framework for Medical Device Evaluation
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.