Review of Simulation Studies Evaluating Imputation Methods in High‐Dimensional Datasets

ABSTRACT High‐dimensional datasets have become increasingly prominent in fields such as medicine, biology, and the social sciences. These datasets often contain missing values, which can significantly compromise the reliability and validity of analytical results. This review examines simulation studies in the literature that address the issue of missing data in high‐dimensional settings. It compares the effectiveness and applicability of multiple imputation (MI) methods, discussing their strengths and weaknesses. The review also explores the challenges encountered by MI techniques in high‐dimensional data and outlines directions for future research. Its primary aim is to guide researchers and practitioners in selecting appropriate MI methods to manage missing data issues in their own datasets. The reviewed simulation studies assess the performance of various MI approaches under different missing data mechanisms and data structures, highlighting their potential advantages and limitations in real‐world applications. In conclusion, MI methods for high‐dimensional data represent a dynamic and rapidly evolving area of research. While existing approaches offer substantial capabilities, there remains a pressing need for continual innovation and rigorous inquiry to develop MI solutions that are theoretically sound, practically feasible, and capable of producing more accurate, reliable, and efficient results. This article is categorized under: Statistical Models > Simulation Models Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical Models > Model Selection