Sure independent screening elastic net for ultra-high dimensional survival data

2021 ◽  
Author(s):  
Ali Hussain AL-Rammahi ◽  
Tahir R. Dikheel
Keyword(s):  
Survival Data ◽  
Elastic Net ◽  
High Dimensional ◽  
Independent Screening

Additive Risk Models for Survival Data with High-Dimensional Covariates

Biometrics ◽  
2005 ◽  
Vol 62 (1) ◽  
pp. 202-210 ◽  
Author(s):  
Shuangge Ma ◽  
Michael R. Kosorok ◽  
Jason P. Fine
Keyword(s):  
Survival Data ◽  
High Dimensional ◽  
Risk Models ◽  
Additive Risk

scholarly journals C-mix: A high-dimensional mixture model for censored durations, with applications to genetic data

2018 ◽  
Vol 28 (5) ◽  
pp. 1523-1539
Author(s):  
Simon Bussy ◽  
Agathe Guilloux ◽  
Stéphane Gaïffas ◽  
Anne-Sophie Jannot
Keyword(s):  
Mixture Model ◽  
Proportional Hazards ◽  
Expectation Maximization Algorithm ◽  
Cox Proportional Hazards ◽  
Elastic Net ◽  
Survival Models ◽  
High Dimensional ◽  
Survival Prediction ◽  
Monte Carlo Simulation Study ◽  
Cox Proportional Hazards Models

We introduce a supervised learning mixture model for censored durations (C-mix) to simultaneously detect subgroups of patients with different prognosis and order them based on their risk. Our method is applicable in a high-dimensional setting, i.e. with a large number of biomedical covariates. Indeed, we penalize the negative log-likelihood by the Elastic-Net, which leads to a sparse parameterization of the model and automatically pinpoints the relevant covariates for the survival prediction. Inference is achieved using an efficient Quasi-Newton Expectation Maximization algorithm, for which we provide convergence properties. The statistical performance of the method is examined on an extensive Monte Carlo simulation study and finally illustrated on three publicly available genetic cancer datasets with high-dimensional covariates. We show that our approach outperforms the state-of-the-art survival models in this context, namely both the CURE and Cox proportional hazards models penalized by the Elastic-Net, in terms of C-index, AUC( t) and survival prediction. Thus, we propose a powerful tool for personalized medicine in cancerology.

scholarly journals High-Dimensional Mediation Analysis Based on Additive Hazards Model for Survival Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Yidan Cui ◽  
Chengwen Luo ◽  
Linghao Luo ◽  
Zhangsheng Yu
Keyword(s):  
Indirect Effect ◽  
Mediation Analysis ◽  
Survival Data ◽  
Cox Model ◽  
The Cancer Genome Atlas ◽  
High Dimensional ◽  
Additive Hazards Model ◽  
Additive Hazards ◽  
Hazards Model ◽  
Positive Rate

Mediation analysis has been extensively used to identify potential pathways between exposure and outcome. However, the analytical methods of high-dimensional mediation analysis for survival data are still yet to be promoted, especially for non-Cox model approaches. We propose a procedure including “two-step” variable selection and indirect effect estimation for the additive hazards model with high-dimensional mediators. We first apply sure independence screening and smoothly clipped absolute deviation regularization to select mediators. Then we use the Sobel test and the BH method for indirect effect hypothesis testing. Simulation results demonstrate its good performance with a higher true-positive rate and accuracy, as well as a lower false-positive rate. We apply the proposed procedure to analyze DNA methylation markers mediating smoking and survival time of lung cancer patients in a TCGA (The Cancer Genome Atlas) cohort study. The real data application identifies four mediate CpGs, three of which are newly found.

scholarly journals Sparse Boosting Based Machine Learning Methods for High-Dimensional Data

2021 ◽  
Author(s):  
Mu Yue
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Variable Selection ◽  
Survival Data ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Learning Methods ◽  
Require Time ◽  
Machine Learning Methods ◽  
Boosting Method

In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.

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