ArticleInternational journal of environmental research and public health2023
Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases.Proceedings of machine learning research · 2025Article
- Comment on: Low-dose glucocorticoid increase the risk of fracture in postmenopausal women with low bone mass: a retrospective cohort study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Predicting adolescent psychopathology from early life factors: A machine learning tutorial.Global epidemiology · 2024Article
- Associations between cardiovascular diseases and cancer mortality: insights from a retrospective cohort analysis of NHANES data.BMC public health · 2024Article
- Associations of different type of physical activity with all-cause mortality in hypertension participants.Scientific reports · 2024Article
- Health-Related Quality of Life is Associated With Pain, Kinesiophobia, and Physical Activity in Individuals Who Underwent Cervical Spine Surgery.Annals of rehabilitation medicine · 2024Article
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Abstract
Sample estimates derived from data with missing values may be unreliable and may negatively impact the inferences that researchers make about the underlying population due to nonresponse bias. As a result, imputation is often preferred to listwise deletion in handling multivariate missing data. In this study, we compared three popular imputation methods: sequential multiple imputation, fractional hot-deck imputation, and generalized efficient regression-based imputation with latent processes for handling multivariate missingness under different missing patterns by conducting descriptive and regression analyses on the imputed data and seeing how the estimates differ from those generated from the full sample. Limited Monte Carlo simulation results by using the National Health Nutrition and Examination Survey and Behavioral Risk Factor Surveillance System are presented to demonstrate the effect of each imputation method on reducing bias and increasing efficiency for the parameter estimate of interest for that particular incomplete variable. Although these three methods did not always outperform listwise deletion in our simulated missing patterns, they improved many descriptive and regression estimates when used to impute all incomplete variables at once.
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