Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis

Dietrich Stefan; Floegel Anna; Troll Martina; Kuehn Tilman; Rathmann Wolfgang; Peters Anette; Sookthai Disorn; von Bergen Martin; Kaaks Rudolf; Adamski Jerzy; Prehn Cornelia; Boeing Heiner; Schulze Matthias B.; Illig Thomas; Pischon Tobias; Knueppel Sven; Wang-Sattler Rui; Drogan Dagmar

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Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis

机译：在实践中随机生存森林：一种模拟复杂代谢组织数据的方法及时分析

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Background: The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues.

机译：背景：代谢组合在预期队列研究中的应用在统计上挑战。鉴于在高度相关的复杂数据中选择疾病相关代谢物的适当统计方法的重要性，我们将随机生存林（RSF）与解决此类问题的自动落后消除程序相结合。

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来源
《International Journal of Epidemiology: Official Journal of the International Epidemiological Association》 |2016年第5期|共15页
作者
Dietrich Stefan; Floegel Anna; Troll Martina; Kuehn Tilman; Rathmann Wolfgang; Peters Anette; Sookthai Disorn; von Bergen Martin; Kaaks Rudolf; Adamski Jerzy; Prehn Cornelia; Boeing Heiner; Schulze Matthias B.; Illig Thomas; Pischon Tobias; Knueppel Sven; Wang-Sattler Rui; Drogan Dagmar;
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作者单位

German Inst Human Nutr Dept Epidemiol Nuthetal Germany;

German Inst Human Nutr Dept Epidemiol Nuthetal Germany;

German Res Ctr Environm Hlth Helmholtz Zentrum Munchen Res Unit Mol Epidemiol Neuherberg Germany;

German Canc Res Ctr Div Canc Epidemiol Heidelberg Germany;

Heinrich Heine Univ Inst Biometr &

Epidemiol Leibniz Ctr Diabet Res Dusseldorf Germany;

German Res Ctr Environm Hlth Helmholtz Zentrum Munchen Inst Epidemiol 2 Neuherberg Germany;

German Canc Res Ctr Div Canc Epidemiol Heidelberg Germany;

Univ Leipzig Dept Mol Syst Biol Helmholtz Ctr Environm Res UFZ Inst Biochem Fac Biosci Pharm &

German Canc Res Ctr Div Canc Epidemiol Heidelberg Germany;

German Ctr Diabet Res DZD Munich Germany;

German Res Ctr Environm Hlth Helmholtz Zentrum Munchen Genome Anal Ctr Inst Expt Genet Munich;

German Inst Human Nutr Dept Epidemiol Nuthetal Germany;

German Ctr Diabet Res DZD Munich Germany;

German Res Ctr Environm Hlth Helmholtz Zentrum Munchen Res Unit Mol Epidemiol Neuherberg Germany;

German Inst Human Nutr Dept Epidemiol Nuthetal Germany;

German Inst Human Nutr Dept Epidemiol Nuthetal Germany;

German Res Ctr Environm Hlth Helmholtz Zentrum Munchen Res Unit Mol Epidemiol Neuherberg Germany;

German Inst Human Nutr Dept Epidemiol Nuthetal Germany;

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原文格式 PDF
正文语种 eng
中图分类流行病学与防疫;
关键词
Cox proportional hazards regression; exploratory survival analysis; multicollinearity; random survival forest; right-censored data; metabolomics; type 2 diabetes mellitus; variable selection;

机译：COX比例危害回归;探索性生存分析;多重药物;随机生存森林;右审查的数据;代谢组学;2型糖尿病;可变选择;

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1. Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis [J] . Dietrich Stefan, Floegel Anna, Troll Martina, International Journal of Epidemiology: Official Journal of the International Epidemiological Association . 2016,第5期

机译：实践中的随机生存森林：一种在事件分析之前对复杂的代谢组学数据建模的方法
2. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data [J] . Justine B. Nasejje, Henry Mwambi, Keertan Dheda, BMC Medical Research Methodology . 2017,第1期

机译：基于模拟研究以及具有时间事件数据的两个应用程序，将条件推断生存森林模型与随机生存森林进行比较
3. Joint modelling of pre-randomisation event counts and multiple post-randomisation survival times with cure rates: application to data for early epilepsy and single seizures [J] . J.K. Rogers, J.L. Hutton Journal of applied statistics . 2013,第3a4期

机译：随机化前事件计数和多个随机化后存活时间与治愈率的联合建模：应用于早期癫痫和单次发作的数据
4. Learning a Complex Metabolomic Dataset using Random Forests and Support Vector Machines [C] . Young Truong, Xiaodong Lin, Chris Beecher Association for Computing Machinery(ACM) Special Interest Group on Knowledge Discovery and Data Mining(SIGKDD) International Conference on Knowledge Discovery and Data Mining; 20040822-20040825; Seattle,WA; US . 2004

机译：使用随机森林和支持向量机学习复杂的代谢组学数据集
5. Novel methodologies for the analysis of complex failure time data and alternative progression -free survival estimators. [D] . Zhang, Jing (Jenny). 2008

机译：用于分析复杂故障时间数据和替代无进展生存估计器的新颖方法。
6. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data [O] . Justine B. Nasejje, Henry Mwambi, Keertan Dheda, 2017

机译：基于模拟研究以及具有时间事件数据的两个应用程序将条件推断生存森林模型与随机生存森林进行比较
7. Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis [O] . Stefan Dietrich, Anna Floegel, Martina Troll, 2016

机译：实践中随机生存林：时间及时建模复杂代谢组织数据的方法

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis

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