Recovery of information from multiple imputation: a simulation study
<p>Abstract</p> <p>Background</p> <p>Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question...
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| Format: | Article |
| Language: | English |
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BMC
2012-06-01
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| Series: | Emerging Themes in Epidemiology |
| Subjects: | |
| Online Access: | http://www.ete-online.com/content/9/1/3 |
| _version_ | 1818353729186824192 |
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| author | Lee Katherine J Carlin John B |
| author_facet | Lee Katherine J Carlin John B |
| author_sort | Lee Katherine J |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question of interest, or whether potential gains may be offset by bias from a poorly fitting imputation model, particularly as the amount of missing data increases.</p> <p>Methods</p> <p>Simulated datasets (n = 1000) drawn from a synthetic population were used to explore information recovery from multiple imputation in estimating the coefficient of a binary exposure variable when various proportions of data (10-90%) were set missing at random in a highly-skewed continuous covariate or in the binary exposure. Imputation was performed using multivariate normal imputation (MVNI), with a simple or zero-skewness log transformation to manage non-normality. Bias, precision, mean-squared error and coverage for a set of regression parameter estimates were compared between multiple imputation and complete case analyses.</p> <p>Results</p> <p>For missingness in the continuous covariate, multiple imputation produced less bias and greater precision for the effect of the binary exposure variable, compared with complete case analysis, with larger gains in precision with more missing data. However, even with only moderate missingness, large bias and substantial under-coverage were apparent in estimating the continuous covariate’s effect when skewness was not adequately addressed. For missingness in the binary covariate, all estimates had negligible bias but gains in precision from multiple imputation were minimal, particularly for the coefficient of the binary exposure.</p> <p>Conclusions</p> <p>Although multiple imputation can be useful if covariates required for confounding adjustment are missing, benefits are likely to be minimal when data are missing in the exposure variable of interest. Furthermore, when there are large amounts of missingness, multiple imputation can become unreliable and introduce bias not present in a complete case analysis if the imputation model is not appropriate. Epidemiologists dealing with missing data should keep in mind the potential limitations as well as the potential benefits of multiple imputation. Further work is needed to provide clearer guidelines on effective application of this method.</p> |
| first_indexed | 2024-12-13T19:14:09Z |
| format | Article |
| id | doaj.art-d4c6a8df3ea34f6ba3568db3a36710f7 |
| institution | Directory Open Access Journal |
| issn | 1742-7622 |
| language | English |
| last_indexed | 2024-12-13T19:14:09Z |
| publishDate | 2012-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Emerging Themes in Epidemiology |
| spelling | doaj.art-d4c6a8df3ea34f6ba3568db3a36710f72022-12-21T23:34:21ZengBMCEmerging Themes in Epidemiology1742-76222012-06-0191310.1186/1742-7622-9-3Recovery of information from multiple imputation: a simulation studyLee Katherine JCarlin John B<p>Abstract</p> <p>Background</p> <p>Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question of interest, or whether potential gains may be offset by bias from a poorly fitting imputation model, particularly as the amount of missing data increases.</p> <p>Methods</p> <p>Simulated datasets (n = 1000) drawn from a synthetic population were used to explore information recovery from multiple imputation in estimating the coefficient of a binary exposure variable when various proportions of data (10-90%) were set missing at random in a highly-skewed continuous covariate or in the binary exposure. Imputation was performed using multivariate normal imputation (MVNI), with a simple or zero-skewness log transformation to manage non-normality. Bias, precision, mean-squared error and coverage for a set of regression parameter estimates were compared between multiple imputation and complete case analyses.</p> <p>Results</p> <p>For missingness in the continuous covariate, multiple imputation produced less bias and greater precision for the effect of the binary exposure variable, compared with complete case analysis, with larger gains in precision with more missing data. However, even with only moderate missingness, large bias and substantial under-coverage were apparent in estimating the continuous covariate’s effect when skewness was not adequately addressed. For missingness in the binary covariate, all estimates had negligible bias but gains in precision from multiple imputation were minimal, particularly for the coefficient of the binary exposure.</p> <p>Conclusions</p> <p>Although multiple imputation can be useful if covariates required for confounding adjustment are missing, benefits are likely to be minimal when data are missing in the exposure variable of interest. Furthermore, when there are large amounts of missingness, multiple imputation can become unreliable and introduce bias not present in a complete case analysis if the imputation model is not appropriate. Epidemiologists dealing with missing data should keep in mind the potential limitations as well as the potential benefits of multiple imputation. Further work is needed to provide clearer guidelines on effective application of this method.</p>http://www.ete-online.com/content/9/1/3Missing dataMultiple imputationFully conditional specificationMultivariate normal imputationNon-normal data |
| spellingShingle | Lee Katherine J Carlin John B Recovery of information from multiple imputation: a simulation study Emerging Themes in Epidemiology Missing data Multiple imputation Fully conditional specification Multivariate normal imputation Non-normal data |
| title | Recovery of information from multiple imputation: a simulation study |
| title_full | Recovery of information from multiple imputation: a simulation study |
| title_fullStr | Recovery of information from multiple imputation: a simulation study |
| title_full_unstemmed | Recovery of information from multiple imputation: a simulation study |
| title_short | Recovery of information from multiple imputation: a simulation study |
| title_sort | recovery of information from multiple imputation a simulation study |
| topic | Missing data Multiple imputation Fully conditional specification Multivariate normal imputation Non-normal data |
| url | http://www.ete-online.com/content/9/1/3 |
| work_keys_str_mv | AT leekatherinej recoveryofinformationfrommultipleimputationasimulationstudy AT carlinjohnb recoveryofinformationfrommultipleimputationasimulationstudy |