A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods.

ABSTRACT: The problem of missing data, particularly for dichotomous variables, is a common issue in medical research. However, few studies have focused on the imputation methods of dichotomous data and their performance, as well as the applicability of these imputation methods and the factors that may affect their performance. In the arrangement of application scenarios, different missing mechanisms, sample sizes, missing rates, the correlation between variables, value distributions, and the number of missing variables were considered. We used data simulation techniques to establish a variety of different compound scenarios for missing dichotomous variables and conducted real-data validation on two real-world medical datasets. We comprehensively compared the performance of eight imputation methods (

SUBMITTER: Ge Y 

PROVIDER: S-EPMC10256703 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature