Project Summary

One of PCORI’s goals is to improve the methods that researchers use for patient-centered outcomes research. PCORI funds methods projects like this one to better understand and advance the use of research methods that improve the strength and quality of comparative effectiveness research.

What is the project about?

Meta-analyses combine results from multiple trials to see how well a treatment works. Data on trials in a meta-analysis are known as aggregate data, or AD. Data on each person in a trial are known as individual participant data, or IPD.

Researchers consider IPD to be the best type of data to use in a meta-analysis. Using IPD allows researchers to answer the questions that are most important to patients. But researchers don’t always have access to IPD. When this occurs, researchers can combine IPD and AD. But questions remain about how to combine IPD and AD when the original trials included different groups of patients or analyzed the data in different ways.

In this study, the research team is looking at how to improve methods for combining IPD and AD in meta-analyses. Doing so may help researchers learn how well treatments work for certain groups of patients when the researchers don’t have access to IPD.

How can this project help improve research methods?

Results may help researchers combine IPD and AD data when conducting meta-analyses.

What is the research team doing?

This study has two parts. In the first part, the research team is creating a method to combine IPD and AD even when the original trials included different groups of people or used different types of analyses.

In the second part, the research team is comparing the new method to existing ones to see if it produces accurate results. The team is also developing software to help other researchers use the new method.

Research methods at a glance

Design ElementDescription
Goal
  • Develop an IPD-AD integrated random effects meta-analysis methodology 
  • Evaluate the methodology against existing comparators
  • Produce an R package to facilitate adoption of the proposed methodology 
ApproachRandom effects modeling, Monte Carlo simulation

*Methods to Support Innovative Research on AI and Large Language Models Supplement
This study received supplemental funding to build on existing PCORI-funded comparative clinical effectiveness research (CER) methods studies to improve understanding of emerging innovations in large language models (LLMs).

Project Information

Mi-Ok Kim, PhD
The Regents of the University of California, San Francisco
$1,058,322
Mixed Data Meta-analysis: Integration of Individual Participant and Aggregate Data

Key Dates

July 2021
April 2026
2021

Tags

Award Type
State State The state where the project originates, or where the primary institution or organization is located. View Glossary
Last updated: March 14, 2024