MEDDDICAL has released a guide addressing a critical gap in pharmaceutical Real-World Evidence: how to evaluate and specify longitudinal data requirements before engaging vendors. The guide provides RWE Directors with actionable methodology for defining what constitutes a fit-for-purpose dataset, directly tackling the frequent mismatch between what companies request and what data providers actually deliver. Rather than offering generic best practices, the resource focuses on concrete specifications that can be applied immediately to vendor conversations and data procurement processes.
More details can be found at https://medddical.com/real-world-evidence-strategy-and-analytics/longitudinal-data-analysis-pharma-dataset-fit-for-purpose/
The stakes for getting longitudinal data specifications right have never been higher. FDA inspection data reveals a 43% increase in warning letter rates between 2019 and 2023, with data integrity issues representing a consistent category of violations. In a widely cited 2019 case, US-based pharma company Zogenix received an FDA refusal-to-file letter for its seizure-control drug, a rejection that triggered a share price fall of nearly 23%, with the FDA citing, among other issues, the submission of an incorrect version of a clinical dataset. The case illustrates how data handling failures can contribute to regulatory rejection with direct financial consequences. For RWE Directors working with observational data, clarity around longitudinal dataset requirements has shifted from a technical preference to a risk-mitigation imperative.
Among the methodological gaps the guide addresses, index date definition stands out as both critical and commonly misunderstood. Simulation studies demonstrate that different time-zero settings can introduce significant bias, producing contradictory conclusions even when the same dataset is analysed for identical research questions. Because this bias compounds throughout the analysis if not addressed upfront, the guide provides RWE Directors with specific language for articulating index date parameters during initial vendor discussions, preventing the costly rework and analytical dead ends that vague specifications routinely produce.
Attrition and censoring schemes represent another area where imprecise requirements lead to regulatory failure. Research shows that inappropriate censoring can substantially bias median survival estimates, while data missingness remains a recurring cause of RWE submission rejection by both FDA and EMA. The guide provides concrete language for specifying attrition handling and censoring requirements before procurement begins, ensuring that the dataset contracted aligns with analytical needs rather than forcing retrospective compromises that undermine study validity.
Understanding what longitudinal depth actually means when evaluating a data source forms a central framework within the guide. Many adverse drug reactions and long-term health consequences manifest years or decades after initial exposure, requiring true continuity of patient observation rather than fragmented follow-up periods. European registries and health insurance claims databases can enable patient follow-up of ten years or longer, but vendor claims about longitudinal coverage vary widely in what they actually deliver at the level of coding completeness, attrition rates, and data continuity. The guide defines minimum follow-up periods and data continuity standards that distinguish genuine longitudinal capability from superficial claims, equipping RWE Directors to evaluate vendor offerings with precision rather than relying on headline coverage figures.
Taken together, the guide gives RWE Directors four things they can use immediately: a specification framework for minimum follow-up periods, concrete requirements language for attrition and censoring handling, a structured approach to index date definition that can be dropped into vendor briefs, and a set of evaluation criteria for assessing longitudinal depth claims.
Organisations that apply these specifications before entering vendor negotiations eliminate the most common sources of mismatch that delay studies, inflate costs, and in some cases produce the kind of regulatory consequences that no amount of analytical sophistication can recover from.
The full methodological treatment, including index date methodology, attrition handling approaches, and a vendor communication framework, is covered in MEDDDICAL's post on longitudinal data analysis in pharma: what makes a dataset fit for purpose.
For more information, visit https://longitudinaldata.medddical.com
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ABOUT MEDDDICAL
MEDDDICAL is a Pan-European Real-World Evidence advisory service helping pharmaceutical and MedTech organisations navigate the real-world data landscape. MEDDDICAL provides expert guidance on RWE strategy, data source evaluation, and vendor selection across Continental European markets, connecting organisations with the right data partnerships for their specific use cases through expert-led consultation.
Contact: https://medddical.com/contact/