ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2019
Landmark Linear Transformation Model for Dynamic Prediction with Application to a Longitudinal Cohort Study of Chronic Disease.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 15 citations in OpenAlex.
- Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.Biostatistics (Oxford, England) · 2025Article
- Emerging technologies for early risk stratification and precision management of diabetic kidney disease: a multimodal framework integrating digital phenotypes and clinical biomarkers.Frontiers in endocrinology · 2025Review
- Predicting kidney graft function and failure among kidney transplant recipients.BMC medical research methodology · 2024Article
- DYNAMIC PREDICTION OF RESIDUAL LIFE WITH LONGITUDINAL COVARIATES USING LONG SHORT-TERM MEMORY NETWORKS.The annals of applied statistics · 2023Article
- A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling.Statistics in medicine · 2023Article
- DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES.The annals of applied statistics · 2023Article
- Predicting the risk of a clinical event using longitudinal data: the generalized landmark analysis.BMC medical research methodology · 2023Article
- GPU Accelerated Estimation of a Shared Random Effect Joint Model for Dynamic Prediction.Computational statistics & data analysis · 2022Article
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers.Statistical methods in medical research · 2020Article
- Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.Biometrical journal. Biometrische Zeitschrift · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Dynamic prediction of the risk of a clinical event using longitudinally measured biomarkers or other prognostic information is important in clinical practice. We propose a new class of landmark survival models. The model takes the form of a linear transformation model, but allows all the model parameters to vary with the landmark time. This model includes many published landmark prediction models as special cases. We propose a unified local linear estimation framework to estimate time-varying model parameters. Simulation studies are conducted to evaluate the finite sample performance of the proposed method. We apply the methodology to a dataset from the African American Study of Kidney Disease and Hypertension and predict individual patient's risk of an adverse clinical event.
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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.