ArticleAlzheimer's research & therapy2021
A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer's disease.
Article in Alzheimer's research & therapy, 2021. 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 6 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Smoking hot joint models for attrition bias-XMAR-ks the spot.American journal of epidemiology · 2026Article
- Pattern Mixture Sensitivity Analyses via Multiple Imputations for Non-Ignorable Dropout in Joint Modeling of Cognition and Risk of Dementia.Statistics in medicine · 2025Article
- Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients.Journal of education and health promotion · 2024Article
- Correction: A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer's disease.Alzheimer's research & therapy · 2023Article
- Robust estimation of dementia prevalence from two-phase surveys with non-responders via propensity score stratification.BMC medical research methodology · 2023Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMissing data can complicate the interpretability of a clinical trial, especially if the proportion is substantial and if there are different, potentially outcome-dependent causes.
methodsWe aimed to obtain unbiased estimates, in the presence of a high level of missing data, for the intervention effects in a prodromal Alzheimer's disease trial: the LipiDiDiet study. We used a competing risk joint model that can simultaneously model each patient's longitudinal outcome trajectory in combination with the timing and type of missingness.
resultsUsing the competing risk joint model, we were able to provide unbiased estimates of the intervention effects in the presence of the different types of missingness. For the LipiDiDiet study, the intervention effects remained statistically significant after this correction for the timing and type of missingness.
conclusionMissing data is a common problem in (Alzheimer) clinical trials. It is important to realize that statistical techniques make specific assumptions about the missing data mechanisms. When there are different missing data sources, a competing risk joint model is a powerful method because it can explicitly model the association between the longitudinal data and each type of missingness.
trial registrationDutch Trial Register, NTR1705 . Registered on 9 March 2009.
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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.