How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations
Abstract Background The multiple imputation by chained equations (MICE) is a widely used approach for handling missing data. However, its robustness, especially for high missing proportions in health indicators, is under-researched. The study aimed to provide a preliminary guideline for the choice o...
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2025-02-01
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| Series: | Population Health Metrics |
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| Online Access: | https://doi.org/10.1186/s12963-025-00364-2 |
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| author | K. P. Junaid Tanvi Kiran Madhu Gupta Kamal Kishore Sujata Siwatch |
| author_facet | K. P. Junaid Tanvi Kiran Madhu Gupta Kamal Kishore Sujata Siwatch |
| author_sort | K. P. Junaid |
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| description | Abstract Background The multiple imputation by chained equations (MICE) is a widely used approach for handling missing data. However, its robustness, especially for high missing proportions in health indicators, is under-researched. The study aimed to provide a preliminary guideline for the choice of the extent of missing proportion to impute longitudinal health-related data using the MICE method. Methods The study obtained complete data on five mortality-related health indicators of 100 countries (2015–2019) from the Global Health Observatory. Nine incomplete datasets with missing rates from 10 to 90% were generated and imputed using MICE. The robustness of MICE was assessed through three approaches: comparison of means using the Repeated Measures- Analysis of variance, estimation of evaluation metrics (Root mean square error, mean absolute deviation, Bias, and proportionate variance), and visual inspection of box plots of imputed and non-imputed data. Results The Repeated Measures- Analysis of variance revealed significant differences between complete and imputed data, primarily in imputed data with over 50% missing proportions. Evaluation metrics exhibited ‘high performance’ for the dataset with a 50% missing proportion for various health indicators However, with missing proportions exceeding 70%, the majority of indicators demonstrated a ‘low’ performance level in terms of most evaluation metrics. The visual inspection of the box plot revealed severe variance shrinkage in imputed datasets with missing proportions beyond 70%, corroborating the findings from the evaluation metrics. Conclusion It demonstrates high robustness up to 50% missing values, with marginal deviations from complete datasets. Caution is warranted for missing proportions between 50 and 70%, as moderate alterations are observed. Proportions beyond 70% lead to significant variance shrinkage and compromised data reliability, emphasizing the importance of acknowledging imputation limitations for practical decision-making. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-02-01 |
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| series | Population Health Metrics |
| spelling | doaj-art-ff5448c783f3496798262ae48657d3b42025-02-02T12:36:07ZengBMCPopulation Health Metrics1478-79542025-02-0123111610.1186/s12963-025-00364-2How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equationsK. P. Junaid0Tanvi Kiran1Madhu Gupta2Kamal Kishore3Sujata Siwatch4Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research (PGIMER)Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research (PGIMER)Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research (PGIMER)Department of Biostatistics, Postgraduate Institute of Medical Education and ResearchDepartment of Obstetrics and Gynaecology, Postgraduate Institute of Medical Education and ResearchAbstract Background The multiple imputation by chained equations (MICE) is a widely used approach for handling missing data. However, its robustness, especially for high missing proportions in health indicators, is under-researched. The study aimed to provide a preliminary guideline for the choice of the extent of missing proportion to impute longitudinal health-related data using the MICE method. Methods The study obtained complete data on five mortality-related health indicators of 100 countries (2015–2019) from the Global Health Observatory. Nine incomplete datasets with missing rates from 10 to 90% were generated and imputed using MICE. The robustness of MICE was assessed through three approaches: comparison of means using the Repeated Measures- Analysis of variance, estimation of evaluation metrics (Root mean square error, mean absolute deviation, Bias, and proportionate variance), and visual inspection of box plots of imputed and non-imputed data. Results The Repeated Measures- Analysis of variance revealed significant differences between complete and imputed data, primarily in imputed data with over 50% missing proportions. Evaluation metrics exhibited ‘high performance’ for the dataset with a 50% missing proportion for various health indicators However, with missing proportions exceeding 70%, the majority of indicators demonstrated a ‘low’ performance level in terms of most evaluation metrics. The visual inspection of the box plot revealed severe variance shrinkage in imputed datasets with missing proportions beyond 70%, corroborating the findings from the evaluation metrics. Conclusion It demonstrates high robustness up to 50% missing values, with marginal deviations from complete datasets. Caution is warranted for missing proportions between 50 and 70%, as moderate alterations are observed. Proportions beyond 70% lead to significant variance shrinkage and compromised data reliability, emphasizing the importance of acknowledging imputation limitations for practical decision-making.https://doi.org/10.1186/s12963-025-00364-2Multiple imputationMICEMissing proportionRM-ANOVAMortalityLongitudinal data |
| spellingShingle | K. P. Junaid Tanvi Kiran Madhu Gupta Kamal Kishore Sujata Siwatch How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations Population Health Metrics Multiple imputation MICE Missing proportion RM-ANOVA Mortality Longitudinal data |
| title | How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations |
| title_full | How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations |
| title_fullStr | How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations |
| title_full_unstemmed | How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations |
| title_short | How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations |
| title_sort | how much missing data is too much to impute for longitudinal health indicators a preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations |
| topic | Multiple imputation MICE Missing proportion RM-ANOVA Mortality Longitudinal data |
| url | https://doi.org/10.1186/s12963-025-00364-2 |
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