Data integration: exploiting ratios of parameter estimates from a reduced external model.

ABSTRACT: We consider the situation of estimating the parameters in a generalized linear prediction model, from an internal dataset, where the outcome variable [Formula: see text] is binary and there are two sets of covariates, [Formula: see text] and [Formula: see text]. We have information from an external study that provides parameter estimates for a generalized linear model of [Formula: see text] on [Formula: see text]. We propose a method that makes limited assumptions about the similarity of the distributions in the two study populations. The method involves orthogonalizing the [Formula: see text] variables and then borrowing information about the ratio of the coefficients from the external model. The method is justified based on a new result relating the parameters in a generalized linear mod

SUBMITTER: Taylor JMG 

PROVIDER: S-EPMC9919493 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature