A p-value pooling approach to significance testing with multiple imputation for missing data. KU Leuven
To some or the other extent, almost all data are incomplete. Incomplete data are generally considered to be problematic as they can reduce power and significantly bias the ensuing inferences drawn. Multiple imputation has emerged as one of the state-of-the-art methods for handling missing data. The multiple imputation approach involves creating m datasets with all missing values imputed. Each dataset is then analyzed separately and in the ...