An evaluation of resampling methods for assessment of survival risk prediction in high-dimensional settings

2010 ◽  
Vol 30 (6) ◽  
pp. 642-653 ◽  
Author(s):  
Jyothi Subramanian ◽  
Richard Simon
Keyword(s):  
Risk Prediction ◽  
High Dimensional ◽  
Resampling Methods ◽  
Survival Risk

Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model

2017 ◽  
Vol 27 (9) ◽  
pp. 2842-2858 ◽  
Author(s):  
Takeshi Emura ◽  
Masahiro Nakatochi ◽  
Shigeyuki Matsui ◽  
Hirofumi Michimae ◽  
Virginie Rondeau
Keyword(s):  
Risk Prediction ◽  
Genetic Factors ◽  
Tumour Progression ◽  
Meta Analysis ◽  
Cox Proportional Hazards Model ◽  
High Dimensional ◽  
Survival Prediction ◽  
Prediction Formula ◽  
Dynamic Prediction ◽  
Personalized Risk

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

scholarly journals Survival Risk Prediction Models of Gliomas Based on IDH and 1p/19q

2020 ◽  
Vol 11 (15) ◽  
pp. 4297-4307
Author(s):  
Han Zou ◽  
Chang Li ◽  
Siyi Wanggou ◽  
Xuejun Li
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Survival Risk

scholarly journals Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data

2019 ◽  
Vol 36 (6) ◽  
pp. 1785-1794
Author(s):  
Jun Li ◽  
Qing Lu ◽  
Yalu Wen
Keyword(s):  
Risk Prediction ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
R Package ◽  
Kernel Functions ◽  
Adaptive Lasso ◽  
Supplementary Information ◽  
High Dimensional ◽  
Omics Data ◽  
Modeling Framework

Abstract Motivation The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed. Results We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer’s Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. Availability and implementation The R-package is available at https://github.com/YaluWen/OmicPred. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Improvement Screening for Ultra-High Dimensional Data with Censored Survival Outcomes and Varying Coefficients

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Mu Yue ◽  
Jialiang Li
Keyword(s):  
Risk Prediction ◽  
Survival Data ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Added Value ◽  
Patient Treatment ◽  
High Dimensional ◽  
Varying Coefficients ◽  
Net Reclassification Improvement ◽  
Cox Proportional Hazards Models

AbstractMotivated by risk prediction studies with ultra-high dimensional bio markers, we propose a novel improvement screening methodology. Accurate risk prediction can be quite useful for patient treatment selection, prevention strategy or disease management in evidence-based medicine. The question of how to choose new markers in addition to the conventional ones is especially important. In the past decade, a number of new measures for quantifying the added value from the new markers were proposed, among which the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) stand out. Meanwhile, C-statistics are routinely used to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. In this paper, we will examine these improvement statistics as well as the norm-based approach for evaluating the incremental values of new markers and compare these four measures by analyzing ultra-high dimensional censored survival data. In particular, we consider Cox proportional hazards models with varying coefficients. All measures perform very well in simulations and we illustrate our methods in an application to a lung cancer study.

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