Precision medicine aims to tailor treatments to individual patients based on their specific needs. However, traditional research methods like randomized controlled trials (RCTs) often focus on average treatment effects rather than differences between patient subgroups. Observational studies, on the other hand, can be biased due to factors not measured in the study. This study aims to combine data from both RCTs and observational studies to improve personalized treatment decisions.
The study team’s researchers are developing new methods to estimate treatment effects for specific patient subgroups more accurately. Their approach involves merging data from RCTs, which have strong scientific validity, with data from observational studies to gain the benefits of both types of research. This will help the team better understand how treatments work for different people, leading to more effective and personalized care.
This research addresses some of the challenges in combining data from different studies, such as differences in the information collected or assumptions made about how people participate in trials. By developing new ways to analyze data from both RCTs and observational studies, researchers hope to improve the quality of evidence used to make treatment decisions, especially for patients with rare or unique responses to treatments.
Researchers will test their methods using data from two large research projects, one focused on preventing childhood obesity and the other on the best aspirin dose for patients with heart disease. The team’s work has the potential to improve patient care by making it easier to identify the best treatments for specific groups of people, based on evidence from multiple sources. This could lead to more effective screening and better care delivery for patients with unique treatment needs.
*All proposed projects, including requested budgets and project periods, are approved subject to a programmatic and budget review by PCORI staff and the negotiation of a formal award contract.