  Many observational studies and clinical trials collect various secondary
outcomes that may be highly correlated with the primary endpoint. These
secondary outcomes are often analyzed in secondary analyses separately from the
main data analysis. However, these secondary outcomes can be used to improve
the estimation precision in the main analysis. We propose a method called
Multiple Information Borrowing (MinBo) that borrows information from secondary
data (containing secondary outcomes and covariates) to improve the efficiency
of the main analysis. The proposed method is robust against model
misspecification of the secondary data. Both theoretical and case studies
demonstrate that MinBo outperforms existing methods in terms of efficiency
gain. We apply MinBo to data from the Atherosclerosis Risk in Communities study
to assess risk factors for hypertension.
