  Missing data is a common problem in clinical data collection, which causes
difficulty in the statistical analysis of such data. To overcome problems
caused by incomplete data, we propose a new imputation method called projective
resampling imputation mean estimation (PRIME), which can also address ``the
curse of dimensionality" problem in imputation with less information loss. We
use various sample sizes, missing-data rates, covariate correlations, and noise
levels in simulation studies, and all results show that PRIME outperformes
other methods such as iterative least-squares estimation (ILSE), maximum
likelihood (ML), and complete-case analysis (CC). Moreover, we conduct a study
of influential factors in cardiac surgery-associated acute kidney injury
(CSA-AKI), which show that our method performs better than the other models.
Finally, we prove that PRIME has a consistent property under some regular
conditions.
