ArticleBMC medical informatics and decision making2025
A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's Disease (AD) is an irreversible neurodegenerative disorder that imposes a significant burden on families and society. Timely intervention during the transitional stages from Mild Cognitive Impairment (MCI) to AD can help mitigate this issue. The MCI-to-AD conversion time would be helpful if it could be predicted. Most studies rely on Cox models, which possess certain limitations and do not intuitively forecast the duration until patients with MCI progress to AD. Thus we construct a new dynamic prediction model based on the conditional restricted mean survival time (cRMST) from a time-scale perspective to explore the factors influencing progression to AD in patients with MCI and predict the average time required MCI patients to progress to AD at different time points in the future.
methodsWe construct a new two-stage dynamic prediction model (tRMST model) based on the conditional restricted mean survival time (cRMST) in combination with landmark method to apply in the analysis of the ADNI database.
resultsThe results of the ADNI analysis showed that four variables (Education, MMSE, ADAS-Cog13 and P-tau) have dynamic effects over time. The C-index and the mean prediction error of the cross validation are better than the static RMST model.
conclusionThis study presents a time-scale dynamic prediction model that effectively leverages longitudinal data to identify the dynamic effects of the factors' impact on the outcome over time, thereby assisting physicians in personalizing treatment for patients.
Indexed as
Identifiers
What OpenQuestion holds
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.