Using item response theory as a methodology to impute categorical missing values.

ABSTRACT: Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data. Several imputation techniques have been designed to replace missing data with stand in values. The various approaches have implications for calculating clinical scores, model building and model testing. The work showcased here supports using an Item Response Theory (IRT) based approach for categorical imputation, comparing it against several methodologies currently used in the machine learning field including k-nearest neighbors (kNN), multiple imputed chained equations (MICE) and Amazon Web Services (AWS) deep learning method, DataWig. Analyses comparing these techniques were perfo

SUBMITTER: Kline A 

PROVIDER: S-EPMC12589431 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature