More on Individual Treatment Effects and Research Studies
Researchers are well aware that it can be difficult to parse out which subgroups will experience different treatment responses, even when they have large numbers of studies available to explore. Some of the challenges include the variable quality of past studies, the variety of environmental and biological factors involved, the need for large numbers of research subjects, and the role of self-selection in outcomes.
Researchers are well aware that it can be difficult to parse out which subgroups will experience different treatment responses, even when they have large numbers of studies available to explore. Some of the challenges include the variable quality of past studies, the variety of environmental and biological factors involved, the need for large numbers of research subjects, and the role of self-selection in outcomes.
Dr. Michael Lauer, director of the Division of Cardiovascular Studies at the National Heart, Lung and Blood Institute at the National Institutes of Health, says the best way to account for individual treatment effects, or heterogeneity, is to plan for it before the research begins. He also explains some of the pitfalls that can come from looking for heterogeneity following the completion of a study.
In this brief video, Dr. David Meltzer, director of the Center for Health & the Social Sciences at the University of Chicago, discusses his views on individual treatment effects and how these effects can be accounted for when conducting comparative effectiveness research.
Dr. C. Daniel Mullins, Professor, University of Maryland School of Pharmacy, explains some of the biologic and non-biologic factors that can affect patient responses to therapies, as well as how to determine which of those factors might be the main cause. He also discusses potential policy implications when considering how to address individual treatment effects.
David M. Kent, MD, MSc, associate professor of Medicine, Neurology, and Clinical and Translational Science at Tufts Medical Center, describes the limitations of randomized control trials in identifying individual treatment effects. To offset these limitations, he recommends researchers routinely risk stratify their trial populations since risk modeling is a fundamental determinant of the opportunity for treatment benefit.
Dr. Kent, along with Drs. Lauer and Meltzer, will be among the featured speakers at the Institute of Medicine workshop, "Observational Studies in a Learning Health System," on April 25-26.
To learn more about individual treatment effects, check out videos, presentations and summaries from the National Pharmaceutical Council’s conference, the Myth of Average: Why Individual Patient Differences Matter.