News: Pavela and Allison offer new insight into cause-and-effect relationships among variables

A common challenge of scientific research is an inability to randomly assign subjects to a treatment that varies on only one variable (e.g., assignment of subjects to groups that differ only in a drug provided). The inability to randomly assign variation on a single treatment variable limits the strength of causal inferences made from the data—a common goal of scientific analysis. Greg Pavela, PhD [1], postdoctoral trainee at the Nutrition Obesity Research Center [2], and David B. Allison, PhD [3], distinguished professor and director of UAB’s Nutrition Obesity Research Center—in collaboration with Howard Wiener, PhD [4], Statistician II in the Department of Epidemiology [5], and Kevin R. Fontaine, PhD [6], professor and chair in the Department of Health Behavior [7]—recently introduced the concept of packet randomized experiments (PREs), an experimental design that improves the internal validity of studies by eliminating a class of observed and unobserved potential confounders.