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Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery

One of the main challenges for the successful implementation of health-related quality of life (HRQoL) assessments is missing data. The current study examined the feasibility and validity of a sequential multiple imputation (MI) method to deal with missing values in the longitudinal HRQoL data from...

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Autores principales: Sun, Sun, Luo, Nan, Stenberg, Erik, Lindholm, Lars, Sahlén, Klas-Göran, Franklin, Karl A., Cao, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518315/
https://www.ncbi.nlm.nih.gov/pubmed/36078543
http://dx.doi.org/10.3390/ijerph191710827
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author Sun, Sun
Luo, Nan
Stenberg, Erik
Lindholm, Lars
Sahlén, Klas-Göran
Franklin, Karl A.
Cao, Yang
author_facet Sun, Sun
Luo, Nan
Stenberg, Erik
Lindholm, Lars
Sahlén, Klas-Göran
Franklin, Karl A.
Cao, Yang
author_sort Sun, Sun
collection PubMed
description One of the main challenges for the successful implementation of health-related quality of life (HRQoL) assessments is missing data. The current study examined the feasibility and validity of a sequential multiple imputation (MI) method to deal with missing values in the longitudinal HRQoL data from the Scandinavian Obesity Surgery Registry. All patients in the SOReg who received bariatric surgery between 1 January 2011 and 31 March 2019 (n = 47,653) were included for the descriptive analysis and missingness pattern exploration. The patients who had completed the short-form 36 (SF-36) at baseline (year 0), and one-, two-, and five-year follow-ups were included (n = 3957) for the missingness pattern simulation and the sequential MI analysis. Eleven items of the SF-36 were selected to create the six domains of SF-6D, and the SF-6D utility index of each patient was calculated accordingly. The multiply-imputed variables in previous year were used as input to impute the missing values in later years. The performance of the sequential MI was evaluated by comparing the actual values with the imputed values of the selected SF-36 items and index at all four time points. At the baseline and year 1, where missing proportions were about 20% and 40%, respectively, there were no statistically significant discrepancies between the distributions of the actual and imputed responses (all p-values > 0.05). In year 2, where the missing proportion was about 60%, distributions of the actual and imputed responses were consistent in 9 of the 11 SF-36 items. However, in year 5, where the missing proportion was about 80%, no consistency was found between the actual and imputed responses in any of the SF-36 items. Relatively high missing proportions in HRQoL data are common in clinical registries, which brings a challenge to analyzing the HRQoL of longitudinal cohorts. The experimental sequential multiple imputation method adopted in the current study might be an ideal strategy for handling missing data (even though the follow-up survey had a missing proportion of 60%), avoiding significant information waste in the multivariate analysis. However, the imputations for data with higher missing proportions warrant more research.
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spelling pubmed-95183152022-09-29 Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery Sun, Sun Luo, Nan Stenberg, Erik Lindholm, Lars Sahlén, Klas-Göran Franklin, Karl A. Cao, Yang Int J Environ Res Public Health Article One of the main challenges for the successful implementation of health-related quality of life (HRQoL) assessments is missing data. The current study examined the feasibility and validity of a sequential multiple imputation (MI) method to deal with missing values in the longitudinal HRQoL data from the Scandinavian Obesity Surgery Registry. All patients in the SOReg who received bariatric surgery between 1 January 2011 and 31 March 2019 (n = 47,653) were included for the descriptive analysis and missingness pattern exploration. The patients who had completed the short-form 36 (SF-36) at baseline (year 0), and one-, two-, and five-year follow-ups were included (n = 3957) for the missingness pattern simulation and the sequential MI analysis. Eleven items of the SF-36 were selected to create the six domains of SF-6D, and the SF-6D utility index of each patient was calculated accordingly. The multiply-imputed variables in previous year were used as input to impute the missing values in later years. The performance of the sequential MI was evaluated by comparing the actual values with the imputed values of the selected SF-36 items and index at all four time points. At the baseline and year 1, where missing proportions were about 20% and 40%, respectively, there were no statistically significant discrepancies between the distributions of the actual and imputed responses (all p-values > 0.05). In year 2, where the missing proportion was about 60%, distributions of the actual and imputed responses were consistent in 9 of the 11 SF-36 items. However, in year 5, where the missing proportion was about 80%, no consistency was found between the actual and imputed responses in any of the SF-36 items. Relatively high missing proportions in HRQoL data are common in clinical registries, which brings a challenge to analyzing the HRQoL of longitudinal cohorts. The experimental sequential multiple imputation method adopted in the current study might be an ideal strategy for handling missing data (even though the follow-up survey had a missing proportion of 60%), avoiding significant information waste in the multivariate analysis. However, the imputations for data with higher missing proportions warrant more research. MDPI 2022-08-30 /pmc/articles/PMC9518315/ /pubmed/36078543 http://dx.doi.org/10.3390/ijerph191710827 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Sun
Luo, Nan
Stenberg, Erik
Lindholm, Lars
Sahlén, Klas-Göran
Franklin, Karl A.
Cao, Yang
Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery
title Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery
title_full Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery
title_fullStr Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery
title_full_unstemmed Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery
title_short Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery
title_sort sequential multiple imputation for real-world health-related quality of life missing data after bariatric surgery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518315/
https://www.ncbi.nlm.nih.gov/pubmed/36078543
http://dx.doi.org/10.3390/ijerph191710827
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