title: Focused information criterion on predictive models in personalized medicine.
abstract: Instead of assessing the overall fit of candidate models like the traditional model selection criteria, the focused information criterion focuses attention directly on the parameter of the primary interest and aims to select the model with the minimum estimated mean squared error for the estimate of the focused parameter. In this article we apply the focused information criterion for personalized medicine. By using individual-level information from clinical observations, demographics, and genetics, we obtain the personalized predictive models to make the prognosis and diagnosis individually. The consideration of the heterogeneity among the individuals helps reduce the prediction uncertainty and improve the prediction accuracy. Two real data examples from biomedical research are studied as illustrations.
authors: Medical Science Biostatistics, Amgen Inc, Thousand Oaks, CA, 91320, USA.
authors: Hui
authors: H
authors: Yang
authors: Yutao
authors: Y
authors: Liu
authors: Hua
authors: H
authors: Liang
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D057285:Precision Medicine / Q000379:methods*; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000453:epidemiology
keywords: Heterogeneity; Model selection criterion; Personalized medicine; Predictive model; Prognosis and diagnosis