ArticleBiometrics2021
A multiple robust propensity score method for longitudinal analysis with intermittent missing data.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations.PharmacoEconomics · 2023Pooled it
- A Doubly Robust Approach to Evaluating the Impact of Hospital Length of Stay on Post-Fracture Recovery in Older Adults With Cognitive Impairment.Statistics in medicine · 2026Article
- Neighborhood Deprivation and Days Spent at Home After Fall-Related Hip Fracture.JAMA network open · 2025Article
- A Propensity Score-Weighted Analysis of Short-Term Corticosteroid Therapy for Refractory Pain Following Spontaneous Subarachnoid Hemorrhage.Neurocritical care · 2025Article
- Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD.bioRxiv : the preprint server for biology · 2025Article
- Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes.Statistics in medicine · 2024Article
- Analyzing risk factors for post-acute recovery in older adults with Alzheimer's disease and related dementia: A new semi-parametric model for large-scale medicare claims.Statistics in medicine · 2024Article
- Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes.Biometrics · 2023Article
- Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation.BMC medical research methodology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.
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Registered trials
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.