Do logistic regression and signal detection identify different subgroups at risk? Implications for the design of tailored interventions

Identifying subgroups of high-risk individuals can lead to the development of tailored interventions for those subgroups. This study compared two mult ivariate statistical methods (logistic regression and signal detection) and evaluated their ability to identify subgroups at risk. The methods identif ied similar risk predictors and had similar predictive accuracy in explorat ory and validation samples. However, the 2 methods did not classify individ uals into the same subgroups. Within subgroups, logistic regression identif ied individuals that were homogeneous in outcome but heterogeneous in risk predictors. In contrast, signal detection identified individuals that were homogeneous in both outcome and risk predictors. Because of the ability to identify homogeneous subgroups, signal detection may be more useful than lo gistic regression for designing distinct tailored interventions for subgrou ps of high-risk individuals.