One of the primary social motivations for scientific research is the ability to make better decisions based on the results. But whether it is deciding what material to use in making a solar panel, what antibiotic to use on an infection or when to launch a satellite, most decisions involve weighing multiple factors, all of which interact with one another in determining the best course of action.
Now, researchers at the University of Pennsylvania and the University of Pittsburgh have developed a decision-making model that compares and weights multiple variables in order to predict the optimal choice.
They tested their model on data from a study of patients seeking treatment for depression, who received either cognitive behavioral therapy or medication. By using the model to generate a score for each patient that indicated which treatment was likely to be more effective for him or her, researchers showed an advantage equivalent to that of an effective treatment relative to a placebo.
Called the “predictive advantage index,” this analytic tool could be used not just in personalized medicine but in any decision-making scenario with complex, and potentially conflicting, variables.
“If you pay attention to only one variable, you’re going to make a decision that is only true with all else being equal,” said Robert DeRubeis, professor and chair of the Department of Psychology in Penn’s School of Arts and Sciences. “But we know that all else is not equal. We need to take all of those inequalities into account at once to find out what is likely to work the best.”
Read more at: Phys.org