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Meta-analysis can be considered a multilevel statistical problem, since information within studies is combined in the presence of potential heterogeneity between studies. Here a general multilevel model framework is developed for meta-analysis to combine either summary data or individual patient...
Multilevel models were originally developed to allow linear regression or ANOVA models to be applied to observations that are not mutually independent. This lack of independence commonly arises due to clustering of the units of observations into ‘higher level units’ such as patients in...
This paper reviews applications of the method of multiple imputation to dealing with multilevel data that have several kinds of imperfections. These are classified into two broad categories: missing values and imprecise measurement (corrupted recording). The role of the model describing the data...
Multilevel modelling is a data analysis technique for analysing linear models in samples with a hierarchical or clustered structure. Clustered data are often present in genetic research where family members may either be required or serve a methodological purpose to study hereditary factors....
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