Abstract
Unified approach (Chen and Chen in J R Stat Soc B 62(3):449–460, 2000) uses a working regression model to extract information from auxiliary variables in two-stage study for computing an efficient estimator of regression parameter. As far as we know, the method is limited to deal with missing complete at random data in a simple monotone missing data pattern. In this research, we extend the unified approach to estimate regression models with nonmonotone missing at random data. We describe an inverse probability weighting estimator condition on estimators from a set of working regression models which contains information from incomplete data and auxiliary variables. The proposed method is flexible and can easily accommodate incomplete data and auxiliary variables. We investigate the finite-sample performance of the proposed estimators using simulation studies and further illustrate the estimation method on a case–control study investigating the risk factors of hip fractures.
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Acknowledgements
We thank Professor Donald L. McLeish, Professor Jerald F. Lawless, the associate editor, and the anonymous reviewers for their helpful comments and suggestions. We are grateful to Professor Hua Yun Chen for letting us use the hip fracture data.
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This research was partially supported by grant from the Natural Sciences and Engineering Research Council of Canada (YZ).
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Zhao, Y., Liu, M. Unified approach for regression models with nonmonotone missing at random data. AStA Adv Stat Anal 105, 87–101 (2021). https://doi.org/10.1007/s10182-020-00389-y
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DOI: https://doi.org/10.1007/s10182-020-00389-y