Semiparametric and Bootstrap Imputation in Repeated Measures Data - Mendoza, Miguel E. (Quezon City University of the Philippines, 2023)
ABSTRACT

In clinical trials with randomized controlled designs and with repeated measures, missing data usually occurs when certain protocols are compromised on when measurements are erroneously taken. Multiple Imputation based on the EM algorithm is commonly used but relies on some stringent distributional assumptions. A novel imputation methods is proposed. Simulation studies illustrate the relative advantage of the proposed method over multiple imputation method.
Keywords: Repeated Measures; Multiple Imputation; Semiparametric Model Imputation