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Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design
BACKGROUND: Questionnaires used in longitudinal studies may have questions added or removed over time for numerous reasons. Data missing completely at a follow-up survey is a unique issue for longitudinal studies. While such excluded questions lack information at one follow-up survey, they are colle...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789687/ https://www.ncbi.nlm.nih.gov/pubmed/33407168 http://dx.doi.org/10.1186/s12874-020-01158-w |
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author | Kolaja, Claire A. Porter, Ben Powell, Teresa M. Rull, Rudolph P. |
author_facet | Kolaja, Claire A. Porter, Ben Powell, Teresa M. Rull, Rudolph P. |
author_sort | Kolaja, Claire A. |
collection | PubMed |
description | BACKGROUND: Questionnaires used in longitudinal studies may have questions added or removed over time for numerous reasons. Data missing completely at a follow-up survey is a unique issue for longitudinal studies. While such excluded questions lack information at one follow-up survey, they are collected at other follow-up surveys, and covariances observed at other follow-up surveys may allow for the recovery of the missing data. This study utilized data from a large longitudinal cohort study to assess the efficiency and feasibility of using multiple imputation (MI) to recover this type of information. METHODS: Millennium Cohort Study participants completed the 9-item Patient Health Questionnaire (PHQ) depression module at 2 time points (2004, 2007). The suicidal ideation item in the module was set to missing for the 2007 assessment. Several single-level MI models using different sets of predictors and forms of suicidal ideation were used to compare self-reported values and imputed values for this item in 2007. Additionally, associations with sleep duration and smoking status, which are related constructs, were compared between self-reported and imputed values of suicidal ideation. RESULTS: Among 63,028 participants eligible for imputation analysis, 4.05% reported suicidal ideation on the 2007 survey. The imputation models successfully identified suicidal ideation, with a sensitivity ranging between 34 and 66% and a positive predictive value between 36 and 42%. Specificity remained above 96% and negative predictive value above 97% for all imputed models. Similar associations were found for all imputation models on related constructs, though the dichotomous suicidal ideation imputed from the model using only PHQ depression items yielded estimates that were closest with the self-reported associations for all adjusted analyses. CONCLUSIONS: Although sensitivity and positive predictive value were relatively low, applying MI techniques allowed for inclusion of an otherwise missing variable. Additionally, correlations with related constructs were estimated near self-reported values. Therefore, the other 8 depression items can be used to estimate suicidal ideation that was completely missing from a survey using MI. However, these imputed values should not be used to estimate population prevalence. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-020-01158-w. |
format | Online Article Text |
id | pubmed-7789687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77896872021-01-07 Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design Kolaja, Claire A. Porter, Ben Powell, Teresa M. Rull, Rudolph P. BMC Med Res Methodol Research Article BACKGROUND: Questionnaires used in longitudinal studies may have questions added or removed over time for numerous reasons. Data missing completely at a follow-up survey is a unique issue for longitudinal studies. While such excluded questions lack information at one follow-up survey, they are collected at other follow-up surveys, and covariances observed at other follow-up surveys may allow for the recovery of the missing data. This study utilized data from a large longitudinal cohort study to assess the efficiency and feasibility of using multiple imputation (MI) to recover this type of information. METHODS: Millennium Cohort Study participants completed the 9-item Patient Health Questionnaire (PHQ) depression module at 2 time points (2004, 2007). The suicidal ideation item in the module was set to missing for the 2007 assessment. Several single-level MI models using different sets of predictors and forms of suicidal ideation were used to compare self-reported values and imputed values for this item in 2007. Additionally, associations with sleep duration and smoking status, which are related constructs, were compared between self-reported and imputed values of suicidal ideation. RESULTS: Among 63,028 participants eligible for imputation analysis, 4.05% reported suicidal ideation on the 2007 survey. The imputation models successfully identified suicidal ideation, with a sensitivity ranging between 34 and 66% and a positive predictive value between 36 and 42%. Specificity remained above 96% and negative predictive value above 97% for all imputed models. Similar associations were found for all imputation models on related constructs, though the dichotomous suicidal ideation imputed from the model using only PHQ depression items yielded estimates that were closest with the self-reported associations for all adjusted analyses. CONCLUSIONS: Although sensitivity and positive predictive value were relatively low, applying MI techniques allowed for inclusion of an otherwise missing variable. Additionally, correlations with related constructs were estimated near self-reported values. Therefore, the other 8 depression items can be used to estimate suicidal ideation that was completely missing from a survey using MI. However, these imputed values should not be used to estimate population prevalence. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-020-01158-w. BioMed Central 2021-01-06 /pmc/articles/PMC7789687/ /pubmed/33407168 http://dx.doi.org/10.1186/s12874-020-01158-w Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research Article Kolaja, Claire A. Porter, Ben Powell, Teresa M. Rull, Rudolph P. Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design |
title | Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design |
title_full | Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design |
title_fullStr | Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design |
title_full_unstemmed | Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design |
title_short | Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design |
title_sort | multiple imputation validation study: addressing unmeasured survey data in a longitudinal design |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789687/ https://www.ncbi.nlm.nih.gov/pubmed/33407168 http://dx.doi.org/10.1186/s12874-020-01158-w |
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