ArticleAmerican journal of epidemiology2023
Comparison of Imputation Strategies for Incomplete Longitudinal Data in Life-Course Epidemiology.
Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Estimating the effects of hypothetical loneliness interventions on memory function among middle-aged and older adults in the United States.American journal of epidemiology · 2026Article
- Noninvasive evaluation of treatment-related protein expression status in advanced gastric cancer using dynamic nomograms with dual-energy CT: a multicenter study.European radiology · 2026Article
- The neutrophil-to-albumin ratio is associated with in-hospital mortality in patients with cirrhosis and pneumonia: a multicenter retrospective cohort study.BMC infectious diseases · 2026Article
- Key research priorities in methodological approaches for measuring the exposome and studying its role in the development of dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Investigating associations between long-term poverty exposure and premature mortality: evidence from the National Longitudinal Survey of Youth 1979 prospective cohort.The Lancet. Public health · 2025Article
- Missing Data in OHCA Registries: How Imputation Methods Affect Research Conclusions-Paper I.Journal of clinical medicine · 2025Article
- Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- Beyond the income-achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low-income youth.JCPP advances · 2025Article
- Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study.Journal of urban health : bulletin of the New York Academy of Medicine · 2025Article
- Mid-Life Household Food Insecurity and Subsequent Memory Function and Rate of Decline in Rural South Africa, 2004-2022.Neuroepidemiology · 2025Article
- Neighborhood Socioeconomic Disadvantage Across the Life Course and Premature Mortality.JAMA network open · 2024Article
- Psychometric properties of the Knowledge and Attitudes to Mental Health Scales in a Dutch sample (KAMHS-NL): A comprehensive mental health literacy measure in adolescents.BMC public health · 2024Article
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8 authors.
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Abstract
Incomplete longitudinal data are common in life-course epidemiology and may induce bias leading to incorrect inference. Multiple imputation (MI) is increasingly preferred for handling missing data, but few studies explore MI-method performance and feasibility in real-data settings. We compared 3 MI methods using real data under 9 missing-data scenarios, representing combinations of 10%, 20%, and 30% missingness and missing completely at random, at random, and not at random. Using data from Health and Retirement Study (HRS) participants, we introduced record-level missingness to a sample of participants with complete data on depressive symptoms (1998-2008), mortality (2008-2018), and relevant covariates. We then imputed missing data using 3 MI methods (normal linear regression, predictive mean matching, variable-tailored specification), and fitted Cox proportional hazards models to estimate effects of 4 operationalizations of longitudinal depressive symptoms on mortality. We compared bias in hazard ratios, root mean square error, and computation time for each method. Bias was similar across MI methods, and results were consistent across operationalizations of the longitudinal exposure variable. However, our results suggest that predictive mean matching may be an appealing strategy for imputing life-course exposure data, given consistently low root mean square error, competitive computation times, and few implementation challenges.
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