Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm

Longitudinal data are not uncommon in many disciplines where repeated measurements on a response variable are collected for all subjects. Some intended measurements may not be available for some subjects resulting in a missing data pattern. Dropout pattern occurs when some subjects leave the study prematurely. The missing data pattern is defined as intermittent if a missing value followed by an observed value. When the probability of missingness depends on the missing value, and may be on the observed values, the missing data mechanism is termed as nonrandom. Ignoring the missing values in this case leads to biased inferences. The stochastic EM (SEM) algorithm is proposed and developed to find parameters estimates in the presence of intermittent missing values. Also, in this setting, the Monte Carlo method is developed to find the standard errors of parameters estimates. Finally, the proposed techniques are applied to a real data from the International Breast Cancer Study Group. (C) 2005 Elsevier B.V. All rights reserved.