Researchers are developing advanced statistical frameworks that could use real-world patient data to supplement traditional clinical trials, potentially speeding life-saving drugs to market. This approach, centered on a field called targeted learning, aims to maintain rigorous safety standards while reducing the time and billion-dollar costs currently required for drug development.
The effort addresses a critical bottleneck in medicine. Developing a new drug takes an average of 12 years and can cost between $173 million and $2.6 billion, with only 12% of candidates ultimately gaining approval. A key driver of this timeline and expense is the large, randomized clinical trial, long considered the gold standard for proving a treatment's safety and efficacy. The new methodology seeks to integrate real-world evidence—data from electronic health records, insurance claims, and disease registries—into this process in a scientifically valid way.
At the core of this innovation is causal inference, a branch of statistics that uses mathematical frameworks to distinguish mere correlation from cause-and-effect. Researchers apply this within the targeted learning framework, which combines causal inference with machine learning. This allows them to draw robust conclusions from observational data, for instance by creating simulated control groups to stand in for traditional placebo arms in trials. "Our researchers are developing new methodologies that could make clinical trials faster, more efficient, and more affordable while maintaining the highest standards of scientific rigor," said Dr. Michael C. Lu, Dean of the UC Berkeley School of Public Health.
The work has gained significant traction with regulatory and industry partners. A major initiative launched in 2020 with a $3.2 million research gift from Novo Nordisk has expanded to include collaborations with Harvard, Oxford, and University College London. In 2024, a new $1.5 million partnership with Gilead Sciences began to further investigate applications of real-world evidence. Researchers have also conducted workshops with the FDA for over a decade, exploring how these methods can be adapted for drug safety studies and regulatory decisions.
If successfully implemented, these statistical tools could streamline the development of treatments for cancers, neurodegenerative diseases, and rare conditions where patients have no time to spare. The ongoing partnerships between academia, regulators, and pharmaceutical companies signal a concerted push to modernize the drug approval pathway, offering hope that effective therapies can reach those in need years sooner than previously possible.