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 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.
Kiernan
M
M
Stanford Center for Research in Disease Prevention, Department of Medicine, Stanford University School of Medicine, 730 Welch Road, Suite B, Palo Alto, California 94304, USA. mkiernan@stanford.edu
Kraemer
H C
HC
Winkleby
M A
MA
King
A C
AC
Taylor
C B
CB
D000328:Adult; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005260:Female; D005500:Follow-Up Studies; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D017603:Signal Detection, Psychological; D044465:White People / Q000706:statistics & numerical data