ArticleStatistical methods in medical research2024
Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.
Article in Statistical methods in medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Joint modeling of high-dimensional longitudinal data and survival using supervised low-rank tensor decomposition.Biostatistics (Oxford, England) · 2026Article
- A phase I study of AZD8186 in combination with docetaxel in patients with PTEN-mutated or PIK3CB-mutated advanced solid tumors.ESMO open · 2025Article
- Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach.Statistics in medicine · 2025Article
- A comparative study of methods for dynamic survival analysis.Frontiers in neurology · 2025Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.
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