Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information.

ABSTRACT: We consider a situation where there is rich historical data available for the coefficients and their standard errors in an established regression model describing the association between a binary outcome variable Y and a set of predicting factors X, from a large study. We would like to utilize this summary information for improving estimation and prediction in an expanded model of interest, Y| X, B. The additional variable B is a new biomarker, measured on a small number of subjects in a new dataset. We develop and evaluate several approaches for translating the external information into constraints on regression coefficients in a logistic regression model of Y| X, B. Borrowing from the measurement error literature we establish an approximate relationship

SUBMITTER: Cheng W 

PROVIDER: S-EPMC6519970 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature