Over time, real-world evidence (RWE) has transitioned from a supplementary tool to a key driver in healthcare decision-making, bridging the gap between clinical trials and real-world practice. Recognized by regulatory bodies like the FDA, EMA, NICE, and CADTH, RWE offers insights into intervention effectiveness and safety across diverse populations. However, challenges persist, including credibility concerns highlighted by flawed COVID-19 studies, emphasizing the need for transparent, relevant data selection. To address this, frameworks like SPACE and SPIFD provide structured methodologies to enhance the design, data selection, and credibility of RWE.[1,2]

Since 2021, regulatory and HTA bodies have published new or updated guidelines, necessitating rationalization and transparency of real-world study design and data source selection to ensure fitness-for-purpose in addressing specific research questions. Researchers have, therefore, been exploring tools to meet these standards. Among them is the Structured Preapproval and Postapproval Comparative Study Design Framework to Generate Valid and Transparent Real-World Evidence (SPACE) tool, introduced in 2019.[3,4]

This SPACE framework provides a step-by-step process for identifying elements of real-world study design, minimal criteria to ensure feasibility and validity of data, and documentation of study design decisions, including the planned analysis. This structured approach significantly mitigates bias in research by ensuring that every aspect of the study design is systematically planned and documented, thereby supporting the initial steps in study design to identify suitable data or draft protocol documents.[3,5]

The SPACE framework consists of several key steps that guide researchers through designing credible real-world studies. The first step involves formulating a clear research question that focuses on addressing specific healthcare needs. This is followed by identifying relevant study designs that align with the research question to ensure valid comparisons. Next, researchers assess the feasibility and validity of data sources by evaluating whether they can provide reliable information for answering the research question. Finally, all decision-making processes are documented comprehensively to enhance transparency and reproducibility throughout the study design process.

In 2021, the Structured Process to Identify Fit-for-Purpose Data (SPIFD) was introduced as an extension to the SPACE framework. SPIFD offers a comprehensive, step-by-step process for conducting and documenting systematic data feasibility assessments to ensure data fitness for the research question. By thoroughly assessing the data sources, SPIFD enhances transparency and validity in data selection, crucially reducing bias by ensuring that only the most suitable data sources are used. Together, SPACE and SPIFD frameworks facilitate valid and transparent real-world comparative study design, planned analysis, and data selection, meeting the stringent standards required by regulators, HTAs, and payers.[6]

The SPIFD framework also follows a structured methodology with specific steps to ensure appropriate data selection for RWE studies. First, researchers conduct systematic assessments of potential data sources to evaluate their suitability for addressing the research question. They then evaluate these sources for relevance, quality, and completeness to ensure they meet necessary standards for generating credible evidence. Finally, alignment with regulatory requirements is ensured so that selected datasets comply with applicable guidelines from regulatory bodies like FDA or EMA.[3,5]

One of the most significant roles of the SPACE framework in generating credible RWE data is its focus on minimizing bias through meticulous study design. Adopting a target trial approach allows researchers to simulate the conditions of a randomized controlled trial (RCT) in the context of real-world setting. This practice ensures that the selected study design closely replicates the ideal experimental conditions, and helps to identify and address potential sources of bias at early stages of a study design process. Thus, enhancing the validity of results and making the evidence more reliable for decision-making.[4]

Furthermore, the framework of SPIFD places immense emphasis on systematic assessment of data feasibility. This means that only data fit for the intended purpose shall be used for studies of real-world settings. SPIFD also aids researchers in selecting the most relevant datasets for their studies, by rigorously evaluating candidate data sources for their relevance, quality, and completeness. This thorough vetting process not only enhances transparency in the selection of data but also ensures that the resulting evidence is strong and credible. This alignment with stringent regulatory and HTA standards fosters greater confidence in the use of RWE for critical healthcare decisions.[6]

To evaluate the effectiveness of studies designed using these frameworks, researchers can rely on several key metrics. For example, tracking the “percentage of relevant data sources identified” during feasibility assessments provides insights into how effectively suitable datasets were pinpointed. Similarly, monitoring the “number of potential biases mitigated” during study design helps gauge how well frameworks like SPACE address methodological challenges early on. These metrics provide tangible measures of success when applying these frameworks to RWE studies while ensuring alignment with regulatory expectations.[6]

In 2023, the introduction of SPIFD2 marked a significant advancement by consolidating both the design and data aspects of the original SPACE and SPIFD templates. SPIFD2 ensures that users specify the correct real-world data (RWD) study design before assessing the feasibility of candidate data sources. This comprehensive framework captures potential sources of bias that may arise in the real-world emulation of the target trial, providing a robust mechanism for enhancing the credibility of RWE. By addressing both study design and data assessment in a unified framework, SPIFD2 offers a holistic approach to mitigate bias and improve the reliability of real-world studies.[3]

In conclusion, the SPACE and SPIFD frameworks represent pivotal advancements in the field of real-world evidence generation, offering structured methodologies to tackle the complexities and challenges inherent in RWE studies. Where SPACE facilitates rigorous study design by adopting a target trial approach, the SPIFD framework ensures the selection of fit-for-purpose data through systematic assessment. By guiding study design and data source selection, these frameworks ensure that RWE studies are rigorous, transparent, and aligned with regulatory and HTA standards. The introduction of SPIFD2 in 2023 further enhances these frameworks, aligning them with evolving regulatory and HTA requirements. This unified approach improves the reliability and relevance of RWE, empowering informed healthcare decisions and advancing patient outcomes.

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References:

1. White R. Building trust in real world evidence (RWE): moving transparency in RWE towards the randomized controlled trial standard. Current Medical Research and Opinion. 2023 Dec 2;39(12):1737-41.
2. Winterstein AG, Ehrenstein V, Brown JS, Stürmer T, Smith MY. A road map for peer review of real-world evidence studies on safety and effectiveness of treatments. Diabetes Care. 2023 Aug 1;46(8):1448-54.
3. Gatto NM, Vititoe SE, Rubinstein E, Reynolds RF, Campbell UB. A structured process to identify fit‐for‐purpose study design and data to generate valid and transparent real‐world evidence for regulatory uses. Clinical Pharmacology & Therapeutics. 2023 Jun;113(6):1235-9.
4. Gatto NM, Reynolds RF, Campbell UB. A structured preapproval and postapproval comparative study design framework to generate valid and transparent real‐world evidence for regulatory decisions. Clinical Pharmacology & Therapeutics. 2019 Jul;106(1):103-15.
5. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. American journal of epidemiology. 2016 Apr 15;183(8):758-64.
6. Gatto NM, Campbell UB, Rubinstein E, Jaksa A, Mattox P, Mo J, Reynolds RF. The structured process to identify fit‐for‐purpose data: a data feasibility assessment framework. Clinical Pharmacology & Therapeutics. 2022 Jan;111(1):122-34.