Application of Pattern Mixture Models to Address Missing Data in Longitudinal Data Analysis Using SPSS
Nursing ResearchPublished 1 May 2012
Heesook Son, Erika Friedmann, Sue A. Thomas
Citations39
SJR quartileQ1
SJR score0.78
SNIP0.79
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TL;DR
This study provides an example of statistical procedures for applying a pattern mixture model to evaluate the informativeness of missing data and conduct analyses of data with informative missingness in longitudinal studies using SPSS.
Abstract
Missing data patterns can be incorporated as fixed effects into LMMs to evaluate the contribution of the presence of informative missingness to and control for the effects of missingness on outcomes. Pattern mixture models are a useful method to address the presence and effect of informative missingness in longitudinal studies.
Keywords
Computer ScienceMathematics
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