Published On: 1/31/2025

by Jennifer Christian, PharmD, PhD, FISPE

Maximizing the Value of Real-World Data: The Critical Role of Causal Inference in Pharmaceutical Innovation

In today's complex healthcare landscape, real-world evidence (RWE) has emerged as an indispensable asset in assessing and informing every stage of pharmaceutical drug development. Beyond its traditional role in post-market surveillance, RWE is now being leveraged to inform critical decisions throughout the drug development lifecycle, from early-stage research to late-stage development and commercialization.

As regulatory bodies increasingly recognize the value of RWE, the industry faces a pivotal challenge: how to transform vast, heterogeneous real-world data efficiently into actionable insights that can accelerate the approval of innovative treatments, enhance patient outcomes, and optimize resource allocation. Real-world data presents a variety of data sources, including unstructured physician notes and patient-reported outcomes, however, these sources vary in structure, consistency, and completeness. Real-world data can have gaps as patients move through health systems and providers. Yet, the data points within complex data sources are critical for leveraging deep analyses in routine clinical care and discussions with regulators. The methodological rigor required to extract meaningful insights from diverse data sources demands cutting-edge analytical approaches and a deep understanding of clinical practice to support the development of new medications.

While randomized controlled trials (RCTs) remain the gold standard for establishing safety and efficacy, they represent only one facet of evidence. The path from demonstrating clinical efficacy and safety to meaningful market presence demands a nuanced understanding of patient populations, treatment patterns, and unmet medical needs—insights that are greatly enhanced by analyses using real-world data. Real-world evidence can demonstrate and confirm a well-defined market and strategy for reaching the people who will benefit most from a new drug or indication.

Increasingly, we are using real-world data to help answer questions that are complex and multi-layered: How do we evaluate the effectiveness and safety of treatments as they are prescribed routinely in practice with minimal bias? How do we optimally use all available data sources to inform the analyses? How do we account for missing data while still having the power to make clear inferences? Generating meaningful RWE can help confirm the safety and effectiveness of treatments as they are used in routine care, provide important insights regarding disease burden, and improve the operations of prospective, clinical studies.

Target RWE has not only pioneered advanced analytics methods to address these types of questions but has proven them through years of case studies led by a team of epidemiologists and statisticians who are redefining how to optimally assess therapies and patient care.

The Power of RWE Done Right: Causal Inference

Real-world evidence and analytics routinely uncover important correlations and trends, but understanding the extent to which medications are causally associated with benefits and risks is required for truly informed decision-making.

Today's research paradigm calls for principled epidemiological approaches that meet the highest scientific standards but also offer the flexibility to adapt to diverse partner needs.  From a practical standpoint, analyses must be able to produce useful and compelling reports, easily transfer insights into publications, and help guide important research presentations that save partners time in interpreting the results.

Target RWE’s proprietary platform causalStudio™ is engineered to answer a broad set of research questions along the drug development lifecycle that no other commercially available analytical software platform can tackle. Target RWE’s in-house statisticians and epidemiologists developed causalStudio™ for researchers needing innovative, causal inference methods to solve multi-faceted questions while minimizing errors in coding and applying rigorous, principled approaches.

Our industry-leading solutions help researchers navigate the complexities of real-world data to unlock cause-and-effect relationships that drive meaningful health-related decisions. With advanced epidemiological and statistical methods, our platform delivers highly accurate and reliable insights, essential for producing regulatory-grade evidence.

Target RWE uniquely offers a comprehensive suite of advanced analytical and causal inference solutions, combining cutting-edge tools with expert services. Here are some additional areas in which Target RWE can help partners meet the increasing demand for principled approaches for observational research:

• Early planning for studies, including comprehensive feasibility assessments
• Validation of outcomes and other key study variables
• Guidance for developing optimal study designs and robust study protocols
• Clinically rich, real-world data with quality and relevancy metrics
• Robust analyses with a gated approach for comparative effectiveness, including negative control outcome studies
• Rigorous quality control throughout the conduct of the study including a clean room committee
• Reporting results through an interactive, web-based platform that enables the researcher to keep all study results, methods, and key decisions in one location

As epidemiology and analytics continue to evolve to address new challenges in healthcare, not all real-world evidence approaches are created equal. Understanding how your RWE strategy and advanced analytics can answer the questions that matter most to your business is crucial.

It's important to work with a strong, analytical team to deliver high-quality results. Contact info@targetrwe.com to learn more about how you may partner with Target RWE!