Prediction Based on Random Survival Forest

Jiang, Shu

doi:10.34297/ajbsr.2019.06.001005

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Specifically, we adopt tree‐based methods in this paper where the possible candidate estimators in step 2 are generated by repeated binary recursive partitions (Ishwaran et al., 2008, 2011). Tree‐based methods facilitate a comprehensive modelling scheme and are appealing for their ability to handle high‐dimensional covariates, facilitate complex and non‐linear relationships between predictors and outcomes, and relax the proportional hazard assumption (Jiang, 2019; Taylor, 2011). Given the tree‐based estimators in step 2, the optimal estimator in step 3 can be selected via cross‐validation by tuning the number of basis functions from step 1 and tree‐based parameters from step 2.…”

Section: Introductionmentioning

confidence: 99%

Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates

Jiang¹,

Xie

Colditz³

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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With the exponential growth in data collection, multiple time‐varying biomarkers are commonly encountered in clinical studies, along with a rich set of baseline covariates. This paper is motivated by addressing a critical issue in the field of Alzheimer’s disease (AD) in which we aim to predict the time for AD conversion in people with mild cognitive impairment to inform prevention and early treatment decisions. Conventional joint models of biomarker trajectory with time‐to‐event data rely heavily on model assumptions and may not be applicable when the number of covariates is large. This motivated us to consider a functional ensemble survival tree framework to characterize the joint effects of both functional and baseline covariates in predicting disease progression. The proposed framework incorporates multivariate functional principal component analysis to characterize the changing patterns of multiple time‐varying neurocognitive biomarker trajectories and then nest these features within an ensemble survival tree in predicting the progression of AD. We provide a fast implementation of the algorithm that accommodates personalized dynamic prediction that can be updated as new observations are gathered to reflect the patient’s latest prognosis. The algorithm is empirically shown to perform well in simulation studies and is illustrated through the analysis of data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (http://adni.loni.usc.edu/). We provide implementation of our proposed method in an R package funest.

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Section: Introductionmentioning

confidence: 99%

Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates

Jiang¹,

Xie

Colditz³

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

Self Cite

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show abstract

“…It works on high dimensional data where the number of covariates exceeds the number of the observations. Also it can handle data that consist of complex and non-linear relationships between the dependant and the independent variables and when the covariates violate the proportional hazard assumption [ 35 ]. There are several advantageous of using the RSF method, such as, it is not based on any model assumption compared to Cox PH model.…”

Section: Methodsmentioning

confidence: 99%

Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data

et al. 2021

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Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model’s predictive performance. In addition, Cox PH predictive performance was better than RSF.

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“…Specifically, we adopt tree-based methods in this paper where the possible candidate estimators in step 2 are generated by repeated binary recursive partitions [Ishwaran et al, 2008[Ishwaran et al, , 2011. Tree-based methods facilitate a comprehensive modeling scheme and are appealing for their ability to handle data with high-dimensional covariates, facilitate complex and nonlinear relationship between predictors and outcomes and relax the proportional hazard assumption [Taylor, 2011, Jiang, 2019. Given the tree-based estimators in step 2, the optimal estimator in step 3 can be selected via cross-validation by tuning the number of functional basis functions from step 1 and treebased parameters which we discuss in detail in Section 2.…”

mentioning

confidence: 99%

Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates

Jiang

Xie

Colditz

2020

Preprint

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With the exponential growth in data collection, multiple timevarying biomarkers are commonly encountered in clinical studies, along with rich set of baseline covariates. This paper is motivated by addressing a critical issue in the field of Alzheimer's disease (AD) in which we aim to predict the time for AD conversion in people with mild cognitive impairment to inform prevention and early treatment decisions. Conventional joint models of biomarker trajectory with time-to-event data rely heavily on model assumptions and may not be applicable when the number of covariates is large. This thus motivated us to consider a functional ensemble survival tree framework to characterize the joint effects of both functional and baseline covariates in predicting disease progression. The proposed framework incorporates multivariate functional principal component analysis to characterize the changing patterns of multiple time-varying neurocognitive biomarker trajectories and then nest these features within an ensemble survival tree in predicting the progression of AD. We provide a fast implementation of the algorithm that accommodates personalized dynamic prediction that can be updated as new observations are gathered to reflect the patient's latest prognosis. The algorithm is empirically shown to perform well in simulation studies and is illustrated through the analysis of data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We provide implementation of our proposed method in R package funest.

show abstract

Prediction Based on Random Survival Forest

Cited by 5 publications

References 14 publications

Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates

Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates

Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data

Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates

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