An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis
- PMID: 36960726
- DOI: 10.1111/biom.13858
An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis
Abstract
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.
Keywords: data integration; empirical likelihood; estimation precision; multiple secondary outcomes; robust inference.
© 2023 The International Biometric Society.
References
REFERENCES
-
- Al-Nozha, M.M., Abdullah, M., Arafah, M.R., Khalil, M.Z., Khan, N.B., Al-Mazrou, Y.Y., Al-Maatouq, M.A., Al-Marzouki, K., Al-Khadra, A., Nouh, M.S., et al. (2007) Hypertension in Saudi Arabia. Saudi Medical Journal, 28(1), 77.
-
- ARIC-Investigators, (1989) The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. American Journal of Epidemiology, 129(4), 687-702.
-
- Chatterjee, N., Chen, Y.H., Maas, P. & Carroll, R.J. (2016) Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources. Journal of the American Statistical Association, 111(513), 107-117.
-
- Chen, C., Han, P. & He, F. (2022) Improving main analysis by borrowing information from auxiliary data. Statistics in Medicine, 41(3), 567-579.
-
- Chen, C., Shen, B., Liu, A., Wu, R. & Wang, M. (2021) A multiple robust propensity score method for longitudinal analysis with intermittent missing data. Biometrics, 77(2), 519-532.
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