Analysis of Longitudinal Data with Missing Values in the Response and Covariates Using the Stochastic EM Algorithm

Year: 2022       Vol.: 71       No.: 1      

Authors: Ahmed M. Gad and Nesma M. Darwish

Abstract:

Longitudinal data are not uncommon in many disciplines where repeated measurements on a response variable are collected for each subject. Missing values are unavoidable in longitudinal studies. Missing values could be in the response variable, the covariates or in both. Dropout pattern occurs when some subjects leave the study prematurely. When the probability of missingness depends on the missing value, and may be on the observed values, the missing data mechanism is termed as non-random. Ignoring the missing values in this case leads to biased inferences. In this paper we will handle missing values in covariates using multiple imputations (MI) and the selection model to fit longitudinal data in the presence of nonrandom dropout. The stochastic EM (Expectation-Maximization) algorithm is developed to obtain the model parameter estimates. Also, parameter estimates of the dropout model have been obtained. Standard errors of estimates have been calculated using the developed Monte Carlo method. The proposed approach performance is evaluated through a simulation study. Also, the proposed approach is applied to a real data set.

Keywords: Interstitial Cystitis data; missing covariates; dropout missingness; multiple imputation; selection model; the SEM algorithm.

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