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

Psychol Methods. 2001 Mar;6(1):35-48. doi: 10.1037/1082-989x.6.1.35.

Abstract

Identifying subgroups of high-risk individuals can lead to the development of tailored interventions for those subgroups. This study compared two multivariate statistical methods (logistic regression and signal detection) and evaluated their ability to identify subgroups at risk. The methods identified similar risk predictors and had similar predictive accuracy in exploratory and validation samples. However, the 2 methods did not classify individuals into the same subgroups. Within subgroups, logistic regression identified 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 logistic regression for designing distinct tailored interventions for subgroups of high-risk individuals.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Cross-Sectional Studies
  • Data Interpretation, Statistical
  • European Continental Ancestry Group / statistics & numerical data
  • Female
  • Follow-Up Studies
  • Hispanic Americans / statistics & numerical data
  • Humans
  • Logistic Models*
  • Male
  • Middle Aged
  • Obesity / epidemiology
  • Risk Assessment / methods*
  • Risk Assessment / statistics & numerical data
  • Signal Detection, Psychological*