Published On: 6/24/2025

Target RWE Advances Next-Generation Causal Inference Approaches in Real-World Evidence with New Publications

DURHAM, N.C., June 23, 2025 /PRNewswire/ -- Target RWE, a leader in clinical evidence generation, has reinforced its position at the forefront of generating real-world evidence (RWE) with the publication of two key epidemiological papers. The publications introduce structured, principled approaches that enhance the validity and applicability of RWE, pushing beyond traditional propensity score methods to deliver more robust and generalizable insights.

Published in Clinical Pharmacology & Therapeutics and titled Advancing Principled Pharmacoepidemiologic Research to Support Regulatory and Healthcare Decision Making: The Era of Real-World Evidence, the first publication details four key areas that are driving the evolution of RWE. This includes the development of increasingly large and rich healthcare datasets that capture comprehensive patient journeys; advanced technologies including artificial intelligence that extract valuable insights from unstructured clinical data; advancements in principled study design and analytical approaches that strengthen causal inference; and robust frameworks that support transparent study conduct and reproducibility.

"Target RWE routinely implements the advanced principled research methodologies that are presented in these publications, which demonstrates our leadership in shaping the future of real word evidence generation. We are not only setting new standards for clinical evidence generation, but also empowering regulators, clinicians, and decision-makers to address critical healthcare challenges with confidence. These studies reflect the transformative impact of RWE in filling evidence gaps, driving innovation, and paving the way for advanced pharmacoepidemiologic research that improves patient outcomes," said Target RWE Chief Scientific Officer Jennifer Christian, PharmD, PhD, FISPE.

The second publication, titled Propensity Score Methods for Confounding Control in Observational Studies of Therapeutics for COVID-19 Infection, addresses the limitations in observational research and propensity score methodologies which historically restrict inference to subpopulations receiving specific treatments - using the COVID-19 pandemic as its setting.

"While propensity score methods represent a significant methodological advancement, it is often not well understood that the interpretation of the results depends on how the methods are used," explained M. Alan Brookhart, PhD, Target RWE Scientific Advisor and co-author of the study. "These issues are particularly important in studies of treatments for COVID-19."

Advanced pharmacoepidemiology methods, including inverse probability of treatment weighted analyses allow researchers to draw inferences about treatment effects across the entire targeted population of interest. Rather than standardizing populations to resemble treatment recipients in matched propensity score analyses, inverting and weighting propensity scores across the entire eligible population enables broader inference.

Target RWE is implementing these methodological advancements across its research platforms by generating real-world data to build comprehensive patient journeys; employing LLMs and AI with manual curation for validation to extract insights from unstructured clinical data; advancing beyond traditional propensity score methods to embrace sophisticated causal inference techniques; and establishing clean room committee approaches for enhanced methodological oversight throughout the conduct of the study.

Further, Target RWE's proprietary software platform, causalStudio™, addresses real-world challenges by providing a software solution that leverages validated analytics proven and trusted for regulatory decision-making. causalStudio™ is comprised of two unique components: causalRisk™ - a software package designed to simplify the complex process of conducting causal inference studies, and causalPHR™ - an intuitive and interactive tool to visualize, stratify, filter, and publish analytical results. Both causalRisk™ and causalPHR™ are data agnostic and developed by statisticians and statistical software developers at Target RWE.

Through the integration of advanced causal inference methodologies and principled epidemiologic approaches, Target RWE ensures that its data and analytics provide a comprehensive and accurate representation of patient experiences in real-world clinical settings.

Contact info@targetrwe.com to learn more about our advanced analytical solutions today!