2019
DOI: 10.32614/rj-2019-028
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jomo: A Flexible Package for Two-level Joint Modelling Multiple Imputation

Abstract: Multiple imputation is a tool for parameter estimation and inference with partially observed data, which is used increasingly widely in medical and social research. When the data to be imputed are correlated or have a multilevel structure-repeated observations on patients, school children nested in classes within schools within educational districts-the imputation model needs to include this structure. Here we introduce our joint modelling package for multiple imputation of multilevel data, jomo, which uses a … Show more

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Cited by 149 publications
(164 citation statements)
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References 22 publications
(33 reference statements)
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“…For meta-analysis of participant level data, a joint modelling approach with multiple imputation (R package jomo) 31 was fitted to the colonisation data, to account for 'missing' data as a result of differences in the risk factors being collected by each individual study. Models fitted to the data assumed an initial fixed common variance matrix across all individual studies.…”
Section: Resultsmentioning
confidence: 99%
“…For meta-analysis of participant level data, a joint modelling approach with multiple imputation (R package jomo) 31 was fitted to the colonisation data, to account for 'missing' data as a result of differences in the risk factors being collected by each individual study. Models fitted to the data assumed an initial fixed common variance matrix across all individual studies.…”
Section: Resultsmentioning
confidence: 99%
“…We used multilevel multiple imputation to impute missing data using the ‘jomo’ package in R (based on a joint multivariate normal modelling approach). 32 Auxiliary variables were total PA (log-transformed), country of birth, language spoken at home, mental health (squared WEMWBS score), BMI z-score, self-rated health, parental involvement and neighbourhood satisfaction. Our imputation model included two levels (adolescents nested in schools).…”
Section: Methodsmentioning
confidence: 99%
“… 78–80 In particular, we will use the R package jomo that uses Markov chain Monte Carlo techniques to draw replacements for the missing values. 81 This procedure is based on a multilevel imputation model that considers associations between continuous and categorical variables both at the level of participants (level 1) and studies (level 2). In addition, it allows for modelling between-study heterogeneity in the covariance matrices, which is especially useful when imputing variables that are completely missing from studies.…”
Section: Methodsmentioning
confidence: 99%