Synthesis Analysis of Regression Models with a Continuous Outcome Xiao-Hua Zhou 1,2, Nan Hu 2, Guizhou Hu3, and Martin Root3 1 HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, WA 98101. 2 Department of Biostatistics, University of Washington, Seattle, WA 98195. 3 BioSignia, Inc., 1822 East NC Highway 54, Suite 350, Durham, NC 27713 To estimate the multivariate regression model from multiple individual studies, it would be challenging to obtain results if the input from individual studies only provide univariate or incomplete multivariate regression information. Samsa et al [1] 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 the data, and it does not provide variance estimates. In this paper we propose a new synthesis method that improves on the existing synthesis method by eliminating the normality assumption, reducing bias, and allowing for the variance estimation of the estimated parameters.



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