Improving estimation and prediction in linear regression incorporating external information from an established reduced model.

ABSTRACT: We consider a situation where there is rich historical data available for the coefficients and their standard errors in a linear regression model describing the association between a continuous outcome variable Y and a set of predicting factors X, from a large study. We would like to use this summary information for improving inference in an expanded model of interest, Y given X,B. The additional variable B is a new biomarker, measured on a small number of subjects in a new dataset. We formulate the problem in an inferential framework where the historical information is translated in terms of nonlinear constraints on the parameter space and propose both frequentist and Bayes solutions to this problem. We show that a Bayesian transformation approach proposed by Gunn and Dunson is a simple a

SUBMITTER: Cheng W 

PROVIDER: S-EPMC5889759 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature