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Multilevel Multiple Imputation in presence of interactions, non-linearities and random slopes

Quartagno, M; Carpenter, J; (2018) Multilevel Multiple Imputation in presence of interactions, non-linearities and random slopes. In: SIS2018: 49th Scientific Meeting of the Italian Statistical Society. (pp. pp. 175-182). : Palermo, Italy. Green open access

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Abstract

Multiple Imputation is a flexible tool to handle missing data that has been increasingly used in recent years. One of the conditions for its validity is that the two models used for (i) imputing and (ii) analysing the data need to be compatible. For example, when the partially observed data have a multilevel structure, both models need to reflect this. Choosing an appropriate imputation model is more complicated when data are missing in a variable included in the substantive multilevel analysis model as a covariate with a random slope, an interaction or a non-linear term. We propose an imputation method based on joint modelling of the partially observed variables. We factor this joint model in two parts: a joint distribution for the covariates, and a conditional distribution for the outcome given the covariates. We guarantee compatibility by using as second term the substantive analysis model. We fit this model with a Gibbs sampler, and we use a Metropolis-Hastings step to accept/reject the proposed draws for the missing values, to guarantee that they are actual random draws from the desired distribution. We show with simulations how this method is up to the job and overcomes competing imputation strategies.

Type: Proceedings paper
Title: Multilevel Multiple Imputation in presence of interactions, non-linearities and random slopes
Event: SIS 2018
Location: Palermo, Italy
Dates: 24 June 2018 - 26 June 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: https://it.pearson.com/content/dam/region-core/ita...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10061898
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