Ambiguity in communication of key study parameters limits the utility of real‐world evidence (RWE) studies in healthcare decision‐making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pds.5507

HARmonized Protocol Template to Enhance Reproducibility of hypothesis evaluating real‐world evidence studies on treatment effects: A good practices report of a joint ISPE/ISPOR task force

HARmonized Protocol Template to Enhance Reproducibility of Hypothesis Evaluating Real-World Evidence Studies on Treatment Effects: A Good Practices Report of a Joint ISPE/ISPOR Task Force.

Generating evidence from real‐world data requires fit‐for‐purpose study design and data. In addition to validity, decision makers require transparency in the reasoning that underlies study design and data source decisions. The 2019 Structured Preapproval and Postapproval Comparative Study Design Framework to Generate Valid and Transparent Real‐World Evidence (SPACE) and the 2021 Structured Process to Identify Fit‐For‐Purpose Data (SPIFD)—intended to be used together—provide a step‐by‐step guide to identify decision grade, fit‐for‐purpose study design and data. In this update (referred to as “SPIFD2” to encompass both the design and data aspects) we provide an update to these frameworks that combines the templates into one, more explicitly calls for articulation of the hypothetical target trial and sources of bias that may arise in the real‐world emulation, and provides explicit references to the Structured Template and Reporting Tool for Real‐World Evidence (STaRT‐RWE) tables that we suggest using immediately after invoking the SPIFD2 framework. Following the steps recommended in the SPIFD2 process requires due diligence on the part of the researcher to ensure that every aspect of study design and data selection is rationalized and supported by evidence. The resulting stepwise documentation enables reproducibility and clear communication with decision makers, and it increases the likelihood that the evidence generated is valid, fit‐for‐purpose, and sufficient to support healthcare and regulatory decisions.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpt.2883

A Structured Process to Identify Fit‐for‐Purpose Study Design and Data to Generate Valid and Transparent Real‐World Evidence for Regulatory Uses

Transparency is increasingly promoted to instill trust in nonrandomized studies using real‐world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.g., a Consolidated Standards of Reporting Trials [CONSORT] like diagram), specific standards on how to maximize validity and transparency with visualization are needed. This paper provides guidance on how to use visualizations throughout the life cycle of a pharmacoepidemiology study—from initial study design to final report—to facilitate rationalized and transparent decision‐making about study design and implementation, and clear communication of study findings. Our intent is to help researchers align their practices with current consensus statements on transparency.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pds.5529

Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting

Studies using real-world data (RWD) can complement evidence from clinical trials and fill evidence gaps during different stages of a medicine's lifecycle. This review presents the experience resulting from the European Medicines Agency (EMA) pilot to generate RWE to support evaluations by EU regulators and down-stream decision makers from September 2021 to February 2023. A total of 61 research topics were identified for RWE generation during this period, covering a wide range of research questions, primarily generating evidence on medicines safety (22, 36%), followed by questions on the design and feasibility of clinical trials (11, 18%), drug utilization (10, 16%), clinical management (10, 16%), and disease epidemiology. A significant number of questions were related to the pediatric population and/or rare diseases. A total of 27 regulatory-led RWD studies have been conducted. Most studies were descriptive and aimed at estimating incidence and prevalence rates of clinical outcomes including adverse events or to evaluate medicines utilization. The review highlights key learnings to guide further efforts to enable the use and establish the value of real-world evidence (RWE) for regulatory decisions. For instance, there is a need to access additional fit-for-purpose and representative data, and to explore further means to provide timely evidence that meets regulatory timelines. The need for early interactions and close collaboration with study requesters, e.g., from the Agency's scientific Committees, to better understand the research question is equally important. Finally, the review provides our perspective on the way forward to maximize the potential of regulatory-led RWE generation. 

Real‐World Evidence to Support EU Regulatory Decision Making—Results From a Pilot of Regulatory Use Cases

To complement real-world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) framework elucidated a process for designing valid and transparent real-world studies. As an extension to SPACE, here, we provide a structured framework for conducting feasibility assessments-a step-by-step guide to identify decision grade, fit-for-purpose data, which complements the United States Food and Drug Administration (FDA)'s framework for a RWE program. The process was informed by our collective experience conducting systematic feasibility assessments of existing data sources for pharmacoepidemiology studies to support regulatory decisions. Used with the SPACE framework, the Structured Process to Identify Fit-For-Purpose Data (SPIFD) provides a systematic process for conducting feasibility assessments to determine if a data source is fit for decision making, helping ensure justification and transparency throughout study development, from articulation of a specific and meaningful research question to identification of fit-for-purpose data and study design.

The Structured Process to Identify Fit-For-Purpose Data: A Data Feasibility Assessment Framework.

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The Structured Process to Identify Fit-For-Purpose Data: A Data Feasibility Assessment Framework.