Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms


Goldstein, H; Carpenter, JR; Browne, WJ; (2014) Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms. Journal of the Royal Statistical Society Series A, (Statistics in Society), 177 (2). pp. 553-564. ISSN 0964-1998 DOI: https://doi.org/10.1111/rssa.12022

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Abstract

The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models. It proposes an efficient Bayesian modelling approach that allows missing values in covariates, including models where there are interactions or other functions of covariates such as polynomials. The procedure can also be used to produce multiply imputed complete data sets. A simulation study is presented as well as the analysis of a longitudinal data set. The paper also shows how existing multiprocess models for handling endogeneity can be extended by the framework proposed.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Research Centre: Centre for Statistical Methodology
Web of Science ID: 331376800011
URI: http://researchonline.lshtm.ac.uk/id/eprint/1635790

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