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A new synthesis analysis method for building logistic regression prediction models
Author(s) -
Sheng Elisa,
Zhou Xiao Hua,
Chen Hua,
Hu Guizhou,
Duncan Ashlee
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6125
Subject(s) - univariate , logistic regression , multivariate statistics , computer science , regression analysis , robustness (evolution) , regression , statistics , multivariate analysis , data mining , econometrics , artificial intelligence , machine learning , mathematics , biochemistry , chemistry , gene
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. Copyright © 2014 John Wiley & Sons, Ltd.