In this free webinar, see how real-world data (RWD) study design can strengthen causal inference and support fit-for-purpose data decisions. Attendees will gain a framework for sequencing research questions, study design and data source selection. The featured speakers will share a structured approach for evaluating whether a dataset is fit for purpose. The speakers will also discuss concrete decision points to apply immediately to RWD studies.

TORONTO, June 24, 2026 /PRNewswire/ — Real-world data (RWD) is increasingly used for causal inference in healthcare research, but generating credible, decision-ready insights requires more than access to data. It demands intentional alignment between the causal question, the study design and the data source. Too often, organizations treat data acquisition as the finish line, when in reality even large, well-curated datasets can produce misleading results if the methods are poorly chosen or the data is mismatched to the question being asked.

This webinar presents a practical framework for designing rigorous RWD studies for causal inference. A foundational premise is that study design and the selection of analytical methods cannot be predetermined in the absence of information about the data source. But starting with the data and working backward to the question is also a common error that even experienced teams make under time and resource pressure.

Attendees will learn how to transparently manage the interplay between question and data using the target trial framework: beginning with a clearly framed research question based on a Target Trial and iteratively adapting it until either finding a viable study design that maps to the available data, or concluding that the data is not fit for purpose.

The seminar will focus on two critical aspects of conducting an RWD study: 1) starting with a strong and structured framework to ask a clear question and study design, and 2) a structured approach for assessing fitness for purpose within the context of the study question and study design. Attendees will leave with practical knowledge and a set of decision points they can apply immediately to their own work.

Register for this webinar to learn how RWD study design can strengthen causal inference and support fit-for-purpose data decisions.

Join Jeffrey S. Brown, PhD, Chief Scientific Officer, TriNetX; and Miguel Hernán, MD, PhD, ScD, Director of CAUSALab, Professor of Biostatistics and Epidemiology, Harvard, for the live webinar on Thursday, July 16, 2026, at 11am EDT (5pm CEST/EU-Central).

For more information, or to register for this event, visit From Data to Evidence: A Framework for RWD Study Design & Causal Inference.

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