Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers

Zhu, Yichen; Huang, Xuelin; Li, Liang

doi:10.1002/bimj.201900112

Cited by 6 publications

(5 citation statements)

References 82 publications

(144 reference statements)

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“…Both JM and LM have many variants to make them flexible and adaptive to the data. For example, the landmark modeling can use functional principal component analysis to extract predictive features from longitudinal history; 27 joint modeling can incorporate flexible functional forms to link the longitudinal trajectory and hazard function 28 . We believe that it would take a lot of real data analyses to fully understand the relative performance of joint modeling and landmark modeling.…”

Section: Discussionmentioning

confidence: 99%

A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling

Astor

2023

Statistics in Medicine

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Joint modeling and landmark modeling are two mainstream approaches to dynamic prediction in longitudinal studies, that is, the prediction of a clinical event using longitudinally measured predictor variables available up to the time of prediction. It is an important research question to the methodological research field and also to practical users to understand which approach can produce more accurate prediction. There were few previous studies on this topic, and the majority of results seemed to favor joint modeling. However, these studies were conducted in scenarios where the data were simulated from the joint models, partly due to the widely recognized methodological difficulty on whether there exists a general joint distribution of longitudinal and survival data so that the landmark models, which consists of infinitely many working regression models for survival, hold simultaneously. As a result, the landmark models always worked under misspecification, which caused difficulty in interpreting the comparison. In this paper, we solve this problem by using a novel algorithm to generate longitudinal and survival data that satisfies the working assumptions of the landmark models. This innovation makes it possible for a "fair" comparison of joint modeling and landmark modeling in terms of model specification. Our simulation results demonstrate that the relative performance of these two modeling approaches depends on the data settings and one does not always dominate the other in terms of prediction accuracy. These findings stress the importance of methodological development for both approaches.The related methodology is illustrated with a kidney transplantation dataset.

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Section: Discussionmentioning

confidence: 99%

A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling

Astor

2023

Statistics in Medicine

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“…In practice, the Karhunen–Loève expansions are truncated at finite orders. A commonly used method is the percentage of variance explained (PVE) method (Dai et al., 2018; Di et al., 2009; Zhu et al., 2020). It chooses a few leading FPCs, which explain a subjectively chosen level of variation, such as 85% or 90%.…”

Section: Methodsmentioning

confidence: 99%

Growth dynamics and heritability for plant high‐throughput phenotyping studies using hierarchical functional data analysis

Qiu

2021

Biometrical J

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In modern high‐throughput plant phenotyping, images of plants of different genotypes are repeatedly taken throughout the growing season, and phenotypic traits of plants (e.g., plant height) are extracted through image processing. It is of interest to recover whole trait trajectories and their derivatives at both genotype and plant levels based on observations made at irregular discrete time points. We propose to model trait trajectories using hierarchical functional principal component analysis (HFPCA) and show that the problem of recovering derivatives of the trajectories is reduced to estimating derivatives of eigenfunctions, which is solved by differentiating eigenequations. Based on HFPCA, we also propose a new measure for the broad‐sense heritability by allowing it to vary over time during plant growth. Simulation studies show that the proposed procedure performs better than its competitors in terms of recovering both trait trajectories and their derivatives. Interesting characteristics of plant growth and heritability dynamics are revealed in the application to a modern plant phenotyping study.

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“…Zhu et al. 13 also used the strict landmarking approach, combining FPCA and Linear Transformation Models for the survival outcomes.…”

Section: Introductionmentioning

confidence: 99%

Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach

Gomon,

Putter,

Fiocco

et al. 2024

Stat Methods Med Res

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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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Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers

Abstract: This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article could fully be reproduced.

Cited by 6 publications

References 82 publications

A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling

A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling

Growth dynamics and heritability for plant high‐throughput phenotyping studies using hierarchical functional data analysis

Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach

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