• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International meeting on computational intelligence methods for bioinformatics and biostatistics >High-Dimensional Sparse Matched Case-Control and Case-Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological Studies
【24h】

High-Dimensional Sparse Matched Case-Control and Case-Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological Studies

机译：高维稀疏匹配的病例对照和病例对照数据：近期工作回顾，R工具的描述以及流行病学研究中的使用说明

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The conditional logistic regression model is the standard tool for the analysis of epidemiological studies in which one or more cases (the event of interest), are matched with one or more controls (not showing the event). These situations arise, for example, in matched case-control and case-crossover studies. In sparse and high-dimensional settings, penalized methods, such as the Lasso, have emerged as an alternative to conventional estimation and variable selection procedures. We describe the R package clogitLasso, which brings together algorithms to estimate parameters of conditional logistic models using sparsity-inducing penalties. Most individually matched designs are covered, and, beside Lasso, Elastic Net, adaptive Lasso and bootstrapped versions are available. Different criteria for choosing the regularization term are implemented, accounting for the dependency of data. Finally, stability is assessed by resampling methods. We previously review the recent works pertaining to clogitLasso. We also report the use in exploratory analysis of a large pharmacoepidemiological study.
机译：条件逻辑回归模型是用于分析流行病学研究的标准工具，其中一个或多个病例（感兴趣的事件）与一个或多个对照（未显示事件）匹配。例如，在匹配的病例对照研究和病例交叉研究中会出现这些情况。在稀疏和高维环境中，惩罚性方法（例如套索法）已经成为传统估计和变量选择程序的替代方法。我们描述了R包clogitLasso，它结合了使用稀疏性惩罚来估计条件逻辑模型参数的算法。涵盖了大多数单独匹配的设计，并且除了套索外，还提供Elastic Net，自适应套索和自举版本。考虑到数据的依赖性，实施了选择正则项的不同标准。最后，通过重采样方法评估稳定性。我们之前曾回顾过有关clogitLasso的最新著作。我们还报告了在大型药物流行病学研究的探索性分析中的用途。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号