Cargando…
Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study
BACKGROUND: When an outcome variable is missing not at random (MNAR: probability of missingness depends on outcome values), estimates of the effect of an exposure on this outcome are often biased. We investigated the extent of this bias and examined whether the bias can be reduced through incorporat...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735815/ https://www.ncbi.nlm.nih.gov/pubmed/29270206 http://dx.doi.org/10.1186/s12982-017-0068-0 |
_version_ | 1783287275223777280 |
---|---|
author | Cornish, R. P. Macleod, J. Carpenter, J. R. Tilling, K. |
author_facet | Cornish, R. P. Macleod, J. Carpenter, J. R. Tilling, K. |
author_sort | Cornish, R. P. |
collection | PubMed |
description | BACKGROUND: When an outcome variable is missing not at random (MNAR: probability of missingness depends on outcome values), estimates of the effect of an exposure on this outcome are often biased. We investigated the extent of this bias and examined whether the bias can be reduced through incorporating proxy outcomes obtained through linkage to administrative data as auxiliary variables in multiple imputation (MI). METHODS: Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) we estimated the association between breastfeeding and IQ (continuous outcome), incorporating linked attainment data (proxies for IQ) as auxiliary variables in MI models. Simulation studies explored the impact of varying the proportion of missing data (from 20 to 80%), the correlation between the outcome and its proxy (0.1–0.9), the strength of the missing data mechanism, and having a proxy variable that was incomplete. RESULTS: Incorporating a linked proxy for the missing outcome as an auxiliary variable reduced bias and increased efficiency in all scenarios, even when 80% of the outcome was missing. Using an incomplete proxy was similarly beneficial. High correlations (> 0.5) between the outcome and its proxy substantially reduced the missing information. Consistent with this, ALSPAC analysis showed inclusion of a proxy reduced bias and improved efficiency. Gains with additional proxies were modest. CONCLUSIONS: In longitudinal studies with loss to follow-up, incorporating proxies for this study outcome obtained via linkage to external sources of data as auxiliary variables in MI models can give practically important bias reduction and efficiency gains when the study outcome is MNAR. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12982-017-0068-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5735815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57358152017-12-21 Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study Cornish, R. P. Macleod, J. Carpenter, J. R. Tilling, K. Emerg Themes Epidemiol Research Article BACKGROUND: When an outcome variable is missing not at random (MNAR: probability of missingness depends on outcome values), estimates of the effect of an exposure on this outcome are often biased. We investigated the extent of this bias and examined whether the bias can be reduced through incorporating proxy outcomes obtained through linkage to administrative data as auxiliary variables in multiple imputation (MI). METHODS: Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) we estimated the association between breastfeeding and IQ (continuous outcome), incorporating linked attainment data (proxies for IQ) as auxiliary variables in MI models. Simulation studies explored the impact of varying the proportion of missing data (from 20 to 80%), the correlation between the outcome and its proxy (0.1–0.9), the strength of the missing data mechanism, and having a proxy variable that was incomplete. RESULTS: Incorporating a linked proxy for the missing outcome as an auxiliary variable reduced bias and increased efficiency in all scenarios, even when 80% of the outcome was missing. Using an incomplete proxy was similarly beneficial. High correlations (> 0.5) between the outcome and its proxy substantially reduced the missing information. Consistent with this, ALSPAC analysis showed inclusion of a proxy reduced bias and improved efficiency. Gains with additional proxies were modest. CONCLUSIONS: In longitudinal studies with loss to follow-up, incorporating proxies for this study outcome obtained via linkage to external sources of data as auxiliary variables in MI models can give practically important bias reduction and efficiency gains when the study outcome is MNAR. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12982-017-0068-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-19 /pmc/articles/PMC5735815/ /pubmed/29270206 http://dx.doi.org/10.1186/s12982-017-0068-0 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Cornish, R. P. Macleod, J. Carpenter, J. R. Tilling, K. Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study |
title | Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study |
title_full | Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study |
title_fullStr | Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study |
title_full_unstemmed | Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study |
title_short | Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study |
title_sort | multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735815/ https://www.ncbi.nlm.nih.gov/pubmed/29270206 http://dx.doi.org/10.1186/s12982-017-0068-0 |
work_keys_str_mv | AT cornishrp multipleimputationusinglinkedproxyoutcomedataresultedinimportantbiasreductionandefficiencygainsasimulationstudy AT macleodj multipleimputationusinglinkedproxyoutcomedataresultedinimportantbiasreductionandefficiencygainsasimulationstudy AT carpenterjr multipleimputationusinglinkedproxyoutcomedataresultedinimportantbiasreductionandefficiencygainsasimulationstudy AT tillingk multipleimputationusinglinkedproxyoutcomedataresultedinimportantbiasreductionandefficiencygainsasimulationstudy |