Analysis of multiple exposures in the case‐crossover design via sparse conditional likelihood
Analysis of multiple exposures in the case‐crossover design via sparse conditional likelihood
复制标题
通过稀疏条件似然分析案例交叉设计中的多重暴露
作者：
M. Avalos;Yves Grandvalet;N. D. Adroher;L. Orriols;E. Lagarde
我们将最小绝对收缩和选择算子(LASSO)和其他稀疏方法(弹性网和LASSO的自举版本)应用到条件Logistic回归模型中，并提供了一个完整的R实现。这些变量选择程序在病例交叉研究的背景下应用。我们通过仿真研究了常规建模策略和稀疏建模策略的性能，并对这些方法在分析老年驾驶员接触药物与造成道路交通事故伤害风险之间的关系的结果进行了实证比较。控制套索式方法的错误发现率仍然是一个问题，但这个问题也存在于传统方法中。稀疏方法具有提供相关性的全局分析的能力，我们得出结论，这里比较的一些变体在具有大量变量的病例交叉研究的背景下是有价值的工具。版权所有©2012 John Wiley&Sons，Ltd.
We adapt the least absolute shrinkage and selection operator (lasso) and other sparse methods (elastic net and bootstrapped versions of lasso) to the conditional logistic regression model and provide a full R implementation. These variable selection procedures are applied in the context of case‐crossover studies. We study the performances of conventional and sparse modelling strategies by simulations, then empirically compare results of these methods on the analysis of the association between exposure to medicinal drugs and the risk of causing an injurious road traffic crash in elderly drivers. Controlling the false discovery rate of lasso‐type methods is still problematic, but this problem is also present in conventional methods. The sparse methods have the ability to provide a global analysis of dependencies, and we conclude that some of the variants compared here are valuable tools in the context of case‐crossover studies with a large number of variables. Copyright © 2012 John Wiley & Sons, Ltd.
影响因子：
5
作者：
MACLURE, M
通讯作者：
MACLURE, M