ArticleStatistics in medicine2022
Landmarking 2.0: Bridging the gap between joint models and landmarking.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Exploratory Modeling of Postoperative Atrial Fibrillation After Cardiac Surgery with Cardiopulmonary Bypass Using Inflammatory Biomarkers and Clinical-Surgical Factors.Medical sciences (Basel, Switzerland) · 2026Article
- Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments.Lifetime data analysis · 2026Article
- Risk Prediction in Cardio-Oncology: Conceptual and Methodological Considerations: JACC: CardioOncology State-of-the-Art Review.JACC. CardioOncology · 2026Review
- Estimating conditional survival benefit for the allocation of scarce resources.Statistical methods in medical research · 2026Article
- Predictive value of baseline levels and early dynamic changes of ctDNA for 28-day mortality in ICU patients with advanced lung cancer: a prospective single-center cohort study.Frontiers in molecular biosciences · 2026Article
- The therapy-conditioned host: a state-transition framework for second primary cancer evolution.Frontiers in oncology · 2026Article
- Optimal risk-assessment scheduling for primary prevention of cardiovascular disease.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates.BMC medical informatics and decision making · 2025Article
- The reporting quality and methodological quality of dynamic prediction models for cancer prognosis.BMC medical research methodology · 2025Article
- A comparative study of methods for dynamic survival analysis.Frontiers in neurology · 2025Article
- Risk-Based Decision Making: Estimands for Sequential Prediction Under Interventions.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Delayed kernels for longitudinal survival analysis and dynamic prediction.Statistical methods in medical research · 2024Article
- Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care using routinely collected electronic health data: a landmarking approach.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Joint models for dynamic prediction in localised prostate cancer: a literature review.BMC medical research methodology · 2022Review
- Landmarking 2.0: Bridging the gap between joint models and landmarking.Statistics in medicine · 2022Article
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2 authors.
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Abstract
The problem of dynamic prediction with time-dependent covariates, given by biomarkers, repeatedly measured over time, has received much attention over the last decades. Two contrasting approaches have become in widespread use. The first is joint modeling, which attempts to jointly model the longitudinal markers and the event time. The second is landmarking, a more pragmatic approach that avoids modeling the marker process. Landmarking has been shown to be less efficient than correctly specified joint models in simulation studies, when data are generated from the joint model. When the mean model is misspecified, however, simulation has shown that joint models may be inferior to landmarking. The objective of this article is to develop methods that improve the predictive accuracy of landmarking, while retaining its relative simplicity and robustness. We start by fitting a working longitudinal model for the biomarker, including a temporal correlation structure. Based on that model, we derive a predictable time-dependent process representing the expected value of the biomarker after the landmark time, and we fit a time-dependent Cox model based on the predictable time-dependent covariate. Dynamic predictions based on this approach for new patients can be obtained by first deriving the expected values of the biomarker, given the measured values before the landmark time point, and then calculating the predicted probabilities based on the time-dependent Cox model. We illustrate the approach in predicting overall survival in liver cirrhosis patients based on prothrombin index.
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