Longitudinal data with dropouts: a comparison of pattern mixture models with complete case analysis
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vor 24 Jahren
Pattern mixture models constitute an alternative to selection
models (Little & Rubin, 1987). Little & Wang (1996)
introduced pattern mixture models for analyzing multivariate normal
longitudinal data with missing values. This paper was the
theoretical foundation and the induce to investigate the small
sample properties of pattern mixture models compared with complete
case analysis. The main point of interest, of the simulations, was
the mean square error of the estimated model parameters. Parameters
estimated by the pattern mixture model are very satisfying under
ignorable mechanism but they have to be scanned carefully under
nonignorable mechanism.
models (Little & Rubin, 1987). Little & Wang (1996)
introduced pattern mixture models for analyzing multivariate normal
longitudinal data with missing values. This paper was the
theoretical foundation and the induce to investigate the small
sample properties of pattern mixture models compared with complete
case analysis. The main point of interest, of the simulations, was
the mean square error of the estimated model parameters. Parameters
estimated by the pattern mixture model are very satisfying under
ignorable mechanism but they have to be scanned carefully under
nonignorable mechanism.
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