基于树的集成方法用于个体化治疗规则
基于树的集成方法用于个体化治疗规则，包含加权随机森林算法，使用CATIE-AD阿尔茨海默病数据。
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
【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
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
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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）。
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
公开数据往往缺乏完整的病理回证与专家标注。千方医数集依托陕川双基地与超十家三甲医院直连，提供完全合规、去标识化、支持注册级质控的专病数据集交易服务。
