ArticleAmerican journal of epidemiology2024
Handling missing data when estimating causal effects with targeted maximum likelihood estimation.
Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the prediction of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Causal Effect Estimation With TMLE: Handling Missing Data and Near Violations of Positivity.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Article
- Treating Hearing Loss With Hearing Aids for the Prevention of Cognitive Decline and Dementia.Neurology · 2026Observational
- REFINE2: a simplified simulation tool to help epidemiologists evaluate the suitability and sensitivity of effect estimation within user-specified data.American journal of epidemiology · 2026Article
- Article
- The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation.Statistics in medicine · 2026Article
- Estimating average causal effects with incomplete exposure and confounders.Journal of causal inference · 2026Article
- The Effect of Treating Hearing Loss with Hearing Aids on Plasma Biomarkers of Alzheimer's Disease and Related Dementias.medRxiv : the preprint server for health sciences · 2025Article
- Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.Statistics in medicine · 2025Article
- Causal estimation of time-varying treatments in observational studies: a scoping review of methods, applications, and missing data practices.BMC medical research methodology · 2025Article
- Antidopaminergic Medications and Clinical Changes in Measures of Huntington's Disease: A Causal Analysis.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Psychological distress in adolescence and later economic and health outcomes in the United States population: A retrospective and modeling study.PLoS medicine · 2025Article
- Using machine-learning methods to identify early-life predictors of 11-year language outcome.Journal of child psychology and psychiatry, and allied disciplines · 2023Article
- Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data.International journal of epidemiology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Targeted maximum likelihood estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on data (1992-1998) from the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate 8 missing-data methods in this context: complete-case analysis, extended TMLE incorporating an outcome-missingness model, the missing covariate missing indicator method, and 5 multiple imputation (MI) approaches using parametric or machine-learning models. We considered 6 scenarios that varied in terms of exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/nonlinear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a nonlinear term. When choosing a method for handling missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and nonlinearities is expected to perform well.
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Registered trials
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