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Simulation driven inferences for multiply imputed longitudinal datasets *
Author(s) -
Demirtas Hakan
Publication year - 2004
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2004.00271.x
Subject(s) - imputation (statistics) , missing data , computer science , econometrics , inference , longitudinal data , causal inference , statistics , data mining , mathematics , machine learning , artificial intelligence
In this article, we demonstrate by simulations that rich imputation models for incomplete longitudinal datasets produce more calibrated estimates in terms of reduced bias and higher coverage rates without duly deflating the efficiency. We argue that the use of supplementary variables that are thought to be potential causes or correlates of missingness or outcomes in the imputation process may lead to better inferential results in comparison to simpler imputation models. The liberal use of these variables is recommended as opposed to the conservative strategy.