A new synthesis analysis method for building logistic regression prediction models | Zendy

Sheng Elisa | Zendy; Zhou Xiao Hua | Zendy; Chen Hua | Zendy; Hu Guizhou | Zendy; Duncan Ashlee | Zendy

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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.

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