Combining multiple imputation and meta-analysis with individual participant data
Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on cov...
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John Wiley & Sons Ltd
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3963448/ |
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pubmed-39634482014-03-25 Combining multiple imputation and meta-analysis with individual participant data Burgess, Stephen White, Ian R Resche-Rigon, Matthieu Wood, Angela M Research Article Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse-variance weighted meta-analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between-study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse-variance weighted meta-analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta-analysis, rather than meta-analyzing each of the multiple imputations and then combining the meta-analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration. John Wiley & Sons Ltd 2013-11-20 2013-05-24 /pmc/articles/PMC3963448/ /pubmed/23703895 http://dx.doi.org/10.1002/sim.5844 Text en © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Burgess, Stephen White, Ian R Resche-Rigon, Matthieu Wood, Angela M |
| spellingShingle |
Burgess, Stephen White, Ian R Resche-Rigon, Matthieu Wood, Angela M Combining multiple imputation and meta-analysis with individual participant data |
| author_facet |
Burgess, Stephen White, Ian R Resche-Rigon, Matthieu Wood, Angela M |
| author_sort |
Burgess, Stephen |
| title |
Combining multiple imputation and meta-analysis with individual participant data |
| title_short |
Combining multiple imputation and meta-analysis with individual participant data |
| title_full |
Combining multiple imputation and meta-analysis with individual participant data |
| title_fullStr |
Combining multiple imputation and meta-analysis with individual participant data |
| title_full_unstemmed |
Combining multiple imputation and meta-analysis with individual participant data |
| title_sort |
combining multiple imputation and meta-analysis with individual participant data |
| description |
Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse-variance weighted meta-analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between-study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse-variance weighted meta-analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta-analysis, rather than meta-analyzing each of the multiple imputations and then combining the meta-analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration. |
| publisher |
John Wiley & Sons Ltd |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3963448/ |
| _version_ |
1612070546446483456 |