Synthesis analysis of regression models with a continuous outcome

ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
Martin Root Ph.D, Associate Professor (Creator)
Appalachian State University (ASU )
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Abstract: To estimate the multivariate regression model from multiple individual studies,it would be challenging to obtain results if the input from individual studiesonly provide univariate or incomplete multivariate regression information.Samsa et al. (J. Biomed. Biotechnol. 2005; 2:113–123) proposed a simple method to combine coefficients from univariate linear regression models into a multivariate linear regression model, a method known as synthesis analysis.However, the validity of this method relies on the normality assumption of thedata, and it does not provide variance estimates. In this paper we propose anew synthesis method that improves on the existing synthesis method byeliminating the normality assumption, reducing bias, and allowing for thevariance estimation of the estimated parameters.

Additional Information

Xiao-Hua Zhou, Nan Hu, Guizhou Hu and Martin Root(2009) Synthesis analysis of regression models with a continuous outcome. Statistics in Medicine (vol.28:1620–1635)
Language: English
Date: 2009
, synthesis, regression, continuous, outcome,

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