基于树的集成方法用于个体化治疗规则-43727
提供基于树集成方法（加权随机森林）推导个体化治疗规则的算法、仿真及阿尔茨海默病真实数据应用范例。
基本信息
资源简介
基于树的集成方法用于个体化治疗规则
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 3 个文件、合计 20.87 KB，主题涵盖 Individualized treatment rule、weighted classification、random forest、predictive biomarkers、treatment selection，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Tree-based ensemble methods for individualized treatment rules》。配套论文《Tree-based ensemble methods for individualized treatment rules》发表于 Biostatistics & Epidemiology（2018），DOI：10.1080/24709360.2018.1435608。项目资助方：National Institutes of Health [grant number R01 GM106177-01].。源站首次发布：2019-05-20。
【数据内容】
源站原始描述：There is a growing interest in the development of statistical methods for personalized medicine or precision medicine, especially for deriving optimal individualized treatment rules (ITRs). An ITR recommends a patient to a treatment based on the patient''s characteristics. The common parametric methods for deriving an optimal ITR, which model the clinical endpoint as a function of the patient''s characteristics, can have suboptimal performance when the conditional mean model is misspecified. Recen…
文件 example.R 为文本内容，开头为：# A demonstration of the functions that implement W-RF library(MASS) data.gen<-function(N=100,nx=15,case="one") { # a function to generate data from # diff…
文件 function.R 为文本内容，开头为：# R implementation of W-RF for # "Tree-based Ensemble Methods For Individualized Treatment Rules" #…
【数据结构与技术规格】
文件数 3 个，合计 20.87 KB。.DS_Store：application/octet-stream，6.15 KB。example.R：text/plain，1.76 KB。function.R：text/plain，12.96 KB。数据集 DOI：10.6084/m9.figshare.8152502.v1。
【主题与分类】
源站学科分类：Space Science、Medicine、Pharmacology、Biotechnology、Biological Sciences not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：Individualized treatment rule、weighted classification、random forest、predictive biomarkers、treatment selection。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/Tree-based_ensemble_methods_for_individualized_treatment_rules/8152502
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Tree-based ensemble methods for individualized treatment rules. Taylor & Francis. Zhu, Kehao; Huang, Ying; Zhou, Xiao-Hua
【使用注意】
· 源站最后修改于 2024-02-15，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
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使用方式
数据集获取（Figshare）
- 源站标题：Tree-based ensemble methods for individualized treatment rules
- DOI：10.6084/m9.figshare.8152502.v1
- 发布日期：2019-05-20
- 文件数：3 个
- 文件总体积：20.87 KB
- 源站页面：https://tandf.figshare.com/articles/dataset/Tree-based_ensemble_methods_for_individualized_treatment_rules/8152502
命令行下载
curl -L -o ".DS_Store" "https://ndownloader.figshare.com/files/15190328"
curl -L -o "example.R" "https://ndownloader.figshare.com/files/15190331"
curl -L -o "function.R" "https://ndownloader.figshare.com/files/15190334"
文件清单
.DS_Store（6.15 KB）example.R（1.76 KB）function.R（12.96 KB）
文件总体积 20.87 KB（源站 API 实测）。
数据说明
There is a growing interest in the development of statistical methods for personalized medicine or precision medicine, especially for deriving optimal individualized treatment rules (ITRs). An ITR recommends a patient to a treatment based on the patient’‘s characteristics. The common parametric methods for deriving an optimal ITR, which model the clinical endpoint as a function of the patient’‘s characteristics, can have suboptimal performance when the conditional mean model is misspecified. Recent methodology development has cast the problem of deriving optimal ITR under a weighted classification framework. Under this weighted classification framework, we develop a weighted random forests (W-RF) algorithm that derives an optimal ITR nonparametrically. In addition, with the W-RF algorithm, we propose the variable importance measures for quantifying relative relevance of the patient’‘s characteristics to treatment selection, and the out-of-bag estimator for the population average outcome under the estimated optimal ITR. Our proposed methods are evaluated through intensive simulation studies. We illustrate the application of our methods using data from Clinical Antipsychotic Trials of Intervention Effectiveness Alzheimer’'s Disease Study.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.8152502.v1（source: https://tandf.figshare.com/articles/dataset/Tree-based_ensemble_methods_for_individualized_treatment_rules/8152502）。
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