ArticleBiometrics2018
Semi-parametric methods of handling missing data in mortal cohorts under non-ignorable missingness.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Long-term memory effects of an incremental blood pressure intervention in a mortal cohort.Biometrics · 2026Article
- A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.Biostatistics (Oxford, England) · 2023Article
- Investigation of the role of sleep and physical activity for chronic disease prevalence and incidence in older Irish adults.BMC public health · 2022Article
- Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropout.Statistical methods in medical research · 2022Article
- Adjusting for selection bias due to missing data in electronic health records-based research.Statistical methods in medical research · 2021Article
- Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Cognition-Mortality Associations Are More Pronounced When Estimated Jointly in Longitudinal and Time-to-Event Models.Frontiers in psychology · 2021Article
- A tractable method to account for high-dimensional nonignorable missing data in intensive longitudinal data.Statistics in medicine · 2020Article
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2 authors.
Funding
Abstract
We propose semi-parametric methods to model cohort data where repeated outcomes may be missing due to death and non-ignorable dropout. Our focus is to obtain inference about the cohort composed of those who are still alive at any time point (partly conditional inference). We propose: i) an inverse probability weighted method that upweights observed subjects to represent subjects who are still alive but are not observed; ii) an outcome regression method that replaces missing outcomes of subjects who are alive with their conditional mean outcomes given past observed data; and iii) an augmented inverse probability method that combines the previous two methods and is double robust against model misspecification. These methods are described for both monotone and non-monotone missing data patterns, and are applied to a cohort of elderly adults from the Health and Retirement Study. Sensitivity analysis to departures from the assumption that missingness at some visit t is independent of the outcome at visit t given past observed data and time of death is used in the data application.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.