title: A new synthesis analysis method for building logistic regression prediction models.
abstract: Synthesis analysis refers to a statistical method that integrates multiple univariate regression models and the correlation between each pair of predictors into a single multivariate regression model. The practical application of such a method could be developing a multivariate disease prediction model where a dataset containing the disease outcome and every predictor of interest is not available. In this study, we propose a new version of synthesis analysis that is specific to binary outcomes. We show that our proposed method possesses desirable statistical properties. We also conduct a simulation study to assess the robustness of the proposed method and compare it to a competing method.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, U.S.A.
authors.forename: Elisa
authors.initials: E
authors.lastname: Sheng
authors.forename: Xiao Hua
authors.initials: XH
authors.lastname: Zhou
authors.forename: Hua
authors.initials: H
authors.lastname: Chen
authors.forename: Guizhou
authors.initials: G
authors.lastname: Hu
authors.forename: Ashlee
authors.initials: A
authors.lastname: Duncan
mesh_terms: D000959:Antihypertensive Agents / Q000627:therapeutic use; D003198:Computer Simulation; D005260:Female; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis
chemical_list: D000959:Antihypertensive Agents
keywords: logistic regression; multivariate analysis; risk assessment; risk factors; risk prediction model; synthesis analysis