Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Li, Cai; Xiao, Luo; Luo, Sheng

doi:10.1111/biom.13427

Cited by 19 publications

(27 citation statements)

References 29 publications

(46 reference statements)

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“…By employing a non-parametric joint modeling, we may be able to capture nonlinear trends and heterogeneous patterns of longitudinal biomarkers in microbiota, as well as negative correlations among taxa (23).…”

Section: Discussionmentioning

confidence: 99%

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

Vehik

et al. 2022

Preprint

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To identify operational taxonomy units (OTUs) signaling disease onset in an observational study, a powerful strategy was selecting participants by matched sets and profiling temporal metagenomes, followed by trajectory analysis. Existing trajectory analyses modeled individual OTU or microbial community without adjusting for the within-community correlation and matched-set-specific latent factors. We proposed a joint model with matching and regularization (JMR) to detect OTU-specific compositional trajectory predictive of host disease status, using nested random effects and covariate taxa pre-selected by Bray-Curtis distance and elastic net regression. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study that generated temporal high-dimensional metagenomic counts with random intercept or slope. Application of the competing methods in the simulated data and the TEDDY cohort showed that JMR outperformed the other methods and identified important taxa in infants’ fecal samples with dynamics preceding host disease status.IMPORTANCEWe proposed a framework to link taxon-specific compositional trajectory and the risk of host developing disease without transformation of the relative abundance. The heterogeneity between matched sets was considered in our model, with improved detection power and well-controlled false positive rate. The inherent negative correlation in microbiota composition due to sum-to-one constraint was adjusted by incorporating the top-correlated taxa as covariate. We designed a simulation pipeline to generate true biomarkers (i.e., causal OTUs and/or those interacting with causal OTUs) for disease onset and the pseudo biomarkers caused by sum-to-one or latent noises. Our method successfully reduced pseudo biomarker rate in simulation results, as well as identified more microbial trajectories signaling autoimmune status of young children enrolled in the TEDDY cohort.

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Section: Discussionmentioning

confidence: 99%

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

Vehik

et al. 2022

Preprint

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“…A promising extension of the current work in JMR is to exploit functional data analysis for multiple microbial trajectories. By employing a non-parametric joint modeling, we may be able to capture nonlinear trends and heterogeneous patterns of longitudinal biomarkers in microbiota, as well as negative correlations among taxa [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

Vehik

et al. 2022

BMC Genomics

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Background To identify operational taxonomy units (OTUs) signaling disease onset in an observational study, a powerful strategy was selecting participants by matched sets and profiling temporal metagenomes, followed by trajectory analysis. Existing trajectory analyses modeled individual OTU or microbial community without adjusting for the within-community correlation and matched-set-specific latent factors. Results We proposed a joint model with matching and regularization (JMR) to detect OTU-specific trajectory predictive of host disease status. The between- and within-matched-sets heterogeneity in OTU relative abundance and disease risk were modeled by nested random effects. The inherent negative correlation in microbiota composition was adjusted by incorporating and regularizing the top-correlated taxa as longitudinal covariate, pre-selected by Bray-Curtis distance and elastic net regression. We designed a simulation pipeline to generate true biomarkers for disease onset and the pseudo biomarkers caused by compositionality. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study generating temporal high-dimensional metagenomic counts with random intercept or slope. Application of the competing methods in the simulated data and the TEDDY cohort showed that JMR outperformed the other methods and identified important taxa in infants’ fecal samples with dynamics preceding host disease status. Conclusion Our method JMR is a robust framework that models taxon-specific trajectory and host disease status for matched participants without transformation of relative abundance, improving the power of detecting disease-associated microbial features in certain scenarios. JMR is available in R package mtradeR at https://github.com/qianli10000/mtradeR.

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“…The joint model (JM) is a broadly applicable approach to jointly analyze longitudinal repeated measurements and survival outcome 7,8 . Recently, extensive research works applied the JM framework to investigate AD progression pattern and predict the risk of dementia onset 9‐11 . In the JM framework, the longitudinal and survival outcomes are commonly modelled using generalized mixed models and Cox proportional hazards models, respectively 8 .…”

Section: Introductionmentioning

confidence: 99%

“…Yao 29 extended the FMEM to the shared latent functional principal components (FPC) model to jointly analyze the univariate functional longitudinal outcome and the survival process. To model the multivariate longitudinal outcomes, Li et al 10 proposed the non‐parametric multivariate functional mixed model (MFMM) that captured nonlinear longitudinal trajectories and simultaneously modelled the survival outcome, linked by the FPC scores that are shared among multiple longitudinal outcomes and the outcome‐specific FPC scores. On the other hand, it is essential to model voxel‐wise sMRI data at baseline as a functional covariate because brain atrophy in some voxels negatively impact cognitive functions 9,11 .…”

Section: Introductionmentioning

confidence: 99%

Multivariate functional mixed model with MRI data: An application to Alzheimer's disease

Zou,

Xiao,

Zeng

et al. 2023

Statistics in Medicine

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Alzheimer's Disease (AD) is the leading cause of dementia and impairment in various domains. Recent AD studies, (ie, Alzheimer's Disease Neuroimaging Initiative (ADNI) study), collect multimodal data, including longitudinal neurological assessments and magnetic resonance imaging (MRI) data, to better study the disease progression. Adopting early interventions is essential to slow AD progression for subjects with mild cognitive impairment (MCI). It is of particular interest to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions. In this article, we propose a multivariate functional mixed model with MRI data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (ie, dementia onset) for subjects with MCI at baseline. Two functional forms (the random-effects model and instantaneous model) linking the longitudinal and survival process are investigated. We use Markov Chain Monte Carlo (MCMC) method based on No-U-Turn Sampling (NUTS)algorithm to obtain posterior samples. We develop a dynamic prediction framework that provides accurate personalized predictions of longitudinal trajectories and survival probability. We apply MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The instantaneous model with voxels from the whole brain has the best prediction performance among all candidate models. The simulation study supports the validity of the estimation and dynamic prediction method.

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Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Cited by 19 publications

References 29 publications

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

Multivariate functional mixed model with MRI data: An application to Alzheimer's disease

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