ArticleBiostatistics (Oxford, England)2018
Methods for handling longitudinal outcome processes truncated by dropout and death.
Article in Biostatistics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Missing not at random in end of life care studies: multiple imputation and sensitivity analysis on data from the ACTION study.BMC medical research methodology · 2021Trial
- Advance care planning in patients with advanced cancer: A 6-country, cluster-randomised clinical trial.PLoS medicine · 2020Trial
- Observational
- Long-term memory effects of an incremental blood pressure intervention in a mortal cohort.Biometrics · 2026Article
- Childhood adversity and disadvantage in relation to incident disability and multimorbidity: prospective analyses of ELSA and two SHARE inception samples.Frontiers in public health · 2026Article
- Prevalence, temporal course and risk factors for phantom eye symptoms in uveal melanoma.Eye (London, England) · 2024Article
- 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
- Adjusting for selection bias due to missing data in electronic health records-based research.Statistical methods in medical research · 2021Article
- Controlling for selective dropout in longitudinal dementia data: Application to the SveDem registry.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2020Article
- Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding.Statistics in medicine · 2019Article
- Anomalous values and missing data in clinical and experimental studies.Jornal vascular brasileiro · 2019Review
- Semi-parametric methods of handling missing data in mortal cohorts under non-ignorable missingness.Biometrics · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Cohort data are often incomplete because some subjects drop out of the study, and inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods that deal with such missing data. In cohort studies of ageing, missing data can arise from dropout or death. Methods that do not distinguish between these reasons for missingness typically provide inference about a hypothetical cohort where no one can die (immortal cohort). It has been suggested that inference about the cohort composed of those who are still alive at any time point (partly conditional inference) may be more meaningful. MI, LI, and IPW can all be adapted to provide partly conditional inference. In this article, we clarify and compare the assumptions required by these MI, LI, and IPW methods for partly conditional inference on continuous outcomes. We also propose augmented IPW estimators for making partly conditional inference. These are more efficient than IPW estimators and more robust to model misspecification. Our simulation studies show that the methods give approximately unbiased estimates of partly conditional estimands when their assumptions are met, but may be biased otherwise. We illustrate the application of the missing data methods using data from the 'Origins of Variance in the Old-old' Twin study.
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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.