Projective Resampling Imputation Mean Estimation Method for Missing Covariates Problem

TL;DR

This paper introduces PRIME, a novel imputation method for missing covariate data that reduces information loss and outperforms existing methods, with proven consistency and successful application in clinical data analysis.

Contribution

The paper proposes PRIME, a new projective resampling imputation method that effectively handles high-dimensional missing data with less information loss and demonstrated superior performance.

Findings

PRIME outperforms ILSE, ML, and CC in simulation studies.

PRIME shows better results in clinical data analysis for CSA-AKI.

PRIME has proven consistency under certain conditions.

Abstract

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.