Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach.
- 1Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy.
- 2School of Mathematics and Statistics, University College Dublin, Dublin, Ireland.
- 3Mathematical Institute, Leiden University, Leiden, the Netherlands.
Abstract
Mixture cure models are widely used in survival analysis when a portion of patients is considered cured and is no longer at risk for the event of interest. In clinical settings, dynamic survival prediction is particularly important to refine prognosis by incorporating patient information updated over time. Landmarking methods have emerged as a flexible approach for this purpose, as they allow to summarize longitudinal covariates up to a given landmark time and use these summaries in subsequent prediction. For mixture cure models, the only landmarking strategy available in the literature relies on summarizing the dynamics of observed longitudinal markers through the last observation carried forward (LOCF) method. However, LOCF discards most of the longitudinal information, does not correct for measurement error, and may rely on outdated values if observation times are far apart. To overcome these limitations, we propose a sequential approach that integrates model-based landmarking within a mixture cure model. Initially, longitudinal covariates are modeled using (generalized) linear mixed models, from which individual-specific summaries are predicted. The predicted summaries are then incorporated as covariates into a Cox proportional hazards cure model. We investigated the performance of the proposed approach under different cure fractions, sample sizes, and longitudinal data structures through an extensive simulation study. The results show that the model-based strategy provides more refined predictions compared to LOCF, even when the model is misspecified in favor of the LOCF approach. Finally, we illustrate our method using a real-world dataset on renal transplant patients.
Related
- A parametric mixture cure rate model with competing risks
- When are time-to-event models a waste of time? Bridging mixture cure models and positive-unlabeled learning for binary classification under right-censoring
- MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning
- PaCMoE: Pairwise Cross-Modal Attention Fusion with Mixture-of-Experts for Stock Movement Prediction
- Dynamic prediction intervals for survival times
BibTeX
@article{cipriani2026dynamic,
title = {{Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach.}},
author = {Marta Cipriani and Marco Alfò and Mirko Signorelli},
journal = {Statistics in medicine},
year = {2026},
month = oct,
doi = {10.1002/sim.70774},
url = {https://europepmc.org/article/MED/42894317},
note = {CC BY},
}
RIS
TY - JOUR
TI - Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach.
AU - Marta Cipriani
AU - Marco Alfò
AU - Mirko Signorelli
PY - 2026
DA - 2026/10/01
JO - Statistics in medicine
DO - 10.1002/sim.70774
UR - https://europepmc.org/article/MED/42894317
AB - Mixture cure models are widely used in survival analysis when a portion of patients is considered cured and is no longer at risk for the event of interest. In clinical settings, dynamic survival prediction is particularly important to refine prognosis by incorporating patient information updated over time. Landmarking methods have emerged as a flexible approach for this purpose, as they allow to summarize longitudinal covariates up to a given landmark time and use these summaries in subsequent prediction. For mixture cure models, the only landmarking strategy available in the literature relies on summarizing the dynamics of observed longitudinal markers through the last observation carried forward (LOCF) method. However, LOCF discards most of the longitudinal information, does not correct for measurement error, and may rely on outdated values if observation times are far apart. To overcome these limitations, we propose a sequential approach that integrates model-based landmarking within a mixture cure model. Initially, longitudinal covariates are modeled using (generalized) linear mixed models, from which individual-specific summaries are predicted. The predicted summaries are then incorporated as covariates into a Cox proportional hazards cure model. We investigated the performance of the proposed approach under different cure fractions, sample sizes, and longitudinal data structures through an extensive simulation study. The results show that the model-based strategy provides more refined predictions compared to LOCF, even when the model is misspecified in favor of the LOCF approach. Finally, we illustrate our method using a real-world dataset on renal transplant patients.
SN - 0277-6715
SN - 1097-0258
ER -