NASHVILLE, Tenn. Douglas Landsittel, PhD addressed researchers at the Meharry-Vanderbilt Alliance on Wednesday, Jan. 27, and discussed "Patient-Centered Outcomes Research (PCOR) and Comparative Effectiveness Research (CER)." Dr. Landsittel, who traveled from the University of Pittsburgh, laid out the general concepts of PCOR and CER, the methods they use and educational efforts taking place on their behalf.
Motivated by the increasing complexity and cost of healthcare, and funding initiatives generated by the American Recovery and Reinvestment Act and the Affordable Care Act, PCOR and CER have gained increasing popularity in medical research.
PCOR has been defined by the Patient-Centered Outcomes Research Institute (PCORI). It helps people make informed healthcare decisions and allows their voices to be heard in assessing the value of healthcare options. It answers the following patient-focused questions:
- "Given my personal characteristics, conditions and preference, what should I expect will happen to me?"
- "What are my options, and what are the benefits and harms of those options?"
- "What can I do to improve the outcomes that are most important to me?"
- "How can the healthcare system improve my chances of achieving the outcomes I prefer?"
CER, meanwhile, has been defined by various groups, including an Institute of Medicine (IOM) Committee in 2009. Those definitions include:
- Directly informing a specific clinical decision from the patient and population perspective.
- Compare at least two alternative interventions, each with potential to be "best practices."
- Describe population and subgroup results.
- Measure outcomes -- both benefit and harm -- important to patients.
- Appropriate methods and data sources.
- Research in real-world settings.
Conducting PCOR and CER introduces substantial challenges, both in terms of the study design and analysis methods.
"Methods matter," Landsittel said. The methods for PCOR and CER begin with study design and pragmatic trials, followed by observational studies, causal inference and a systematic review.
In his talk, Landsittel provided an overview of the associated methodological challenges and related educational efforts. In particular, he described a methods contract funded by PCORI aimed at developing a decision tool for identifying which modeling strategy works best, and the Expanding National Capacity in PCOR through Training (ENACT) program, which is funded by the Agency for Healthcare Research and Quality (AHRQ) to build expertise and infrastructure in Minority-Service Institutions.
Landsittel is a Professor of Medicine, Biostatistics and Clinical and Translational Science at the University of Pittsburgh, where he serves as the Director of Biostatistics for the Starzl Transplant Institute and the Director of the Section on Biomarkers and Prediction Modeling. He also directs the ENACT program, which is an AHRQ-funded collaborative effort with Minority-Serving Institutions to strengthen their expertise and infrastructure in patient-centered outcomes research. His research focuses on causal inference for observational data and design of analysis of biomarker studies. He is the primary investigator of a data coordinating center for an NIDDK consortium and a PCORI contract to evaluate optimal modeling strategies for observational comparative effectiveness research. He has published over 100 papers in the peer-reviewed literature and serves as an appointment member of the Kidney Nutrition, Obesity and Diabetes NIH Study Section and the Journal of Clinical Oncology Editorial Board. He has also served as a reviewer for many other study sections and special emphasis panels for CDC, DOD, NCI, PCORI and other agencies and organizations.
About the Meharry-Vanderbilt Alliance
Founded in 1999, the Alliance bridges the institutions of Meharry Medical College and Vanderbilt University. Its mission is to enrich learning and advance clinical research by developing and supporting mutually beneficial partnerships between Meharry Medical College, Vanderbilt University and the communities they serve.
