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Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm. (English) Zbl 1445.62043

Summary: 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.

MSC:

62F10 Point estimation
62D10 Missing data
62-08 Computational methods for problems pertaining to statistics
65C05 Monte Carlo methods
62P10 Applications of statistics to biology and medical sciences; meta analysis
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