A new synthesis analysis method for building logistic regression prediction models

被引:7
作者
Sheng, Elisa [1 ]
Zhou, Xiao Hua [1 ,2 ]
Chen, Hua [4 ]
Hu, Guizhou [3 ]
Duncan, Ashlee [3 ]
机构
[1] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[2] Renmin Univ China, Sch Stat, Beijing, Peoples R China
[3] BioSignia Inc, Durham, NC USA
[4] Inst Appl Phys & Computat Math, Beijing 100088, Peoples R China
关键词
synthesis analysis; logistic regression; risk prediction model; risk factors; risk assessment; multivariate analysis; FASTING PLASMA-GLUCOSE; DISEASE; RISK;
D O I
10.1002/sim.6125
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 (c) 2014 John Wiley & Sons, Ltd.
引用
收藏
页码:2567 / 2576
页数:10
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