SPACE试验中年龄与治疗交互作用的统计方法研究

SPACE临床试验数据,用于分析年龄与治疗(CAS vs CEA)对症状性颈动脉狭窄患者预后的交互作用。

Hans-Henning EcksteinHans-Henning Eckstein
Taylor & Francis Group
2019-06-11 更新
浏览 15
表格Covariate–treatment interactionpredictive covariatenon-linear interactionindividualized medicinequalitative interaction
Investigation of age–treatment interaction in the SPACE trial using different statistical approaches

基本信息

模态
表格
大小
0.000093664 GB
许可
CC BY 4.0
创建/更新时间
2019-06-11
版本
v2

资源简介

SPACE试验中年龄与治疗交互作用的统计方法研究

【一句话概览】

本数据集是 figshare 公开的结构化表格数据集,收录 2 个文件、合计 93.66 KB,主题涵盖 Covariate–treatment interaction、predictive covariate、non-linear interaction、individualized medicine、qualitative interaction,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《Investigation of age–treatment interaction in the SPACE trial using different statistical approaches》。配套论文《Investigation of age–treatment interaction in the SPACE trial using different statistical approaches》发表于 Journal of Applied Statistics(2018),DOI:10.1080/02664763.2018.1558186。项目资助方:Funding for conduction of the SPACE trial was provided by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung) (01GI9918), the German Research Foundation (Deutsche Forschungsgemeinschaft; HA 1394/4-2 and 1397/4-3), the German Society of Neurology, German Society of Neuroradiology, German Radiological Society, Boston Scientific, Guidant, and Sanofi-Aventis.。源站首次发布:2019-06-11。

【数据内容】

源站原始描述:Selection of treatments to fit the specific needs for a certain patient is one major challenge in modern medicine. Personalized treatments rely on established patient–treatment interactions. In recent years, various statistical methods for the identification and estimation of interactions between relevant covariates and treatment were proposed. In this article, different available methods for detection and estimation of a covariate–treatment interaction for a time-to-event outcome, namely the st…

【数据结构与技术规格】

文件数 2 个,合计 93.66 KB。cjas_a_1558186_sm3046.pdf:application/pdf,64.96 KB。cjas_a_1558186_sm9724.r:text/plain,28.71 KB。数据集 DOI:10.6084/m9.figshare.7477574.v2。

【主题与分类】

源站学科分类:Medicine、Pharmacology、Biotechnology、Mathematical Sciences not elsewhere classified、Cancer。主题标签:Covariate–treatment interaction、predictive covariate、non-linear interaction、individualized medicine、qualitative interaction。

【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/Investigation_of_age_treatment_interaction_in_the_SPACE_trial_using_different_statistical_approaches/7477574

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:Investigation of age–treatment interaction in the SPACE trial using different statistical approaches. Taylor & Francis. Haller, Bernhard; Eckstein, Hans-Henning; Ringleb, Peter A.; Ulm, Kurt

【使用注意】

· 源站最后修改于 2023-05-30,引用时请注意版本时效。

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

帮我联系

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。

免登录

有偿下载

公开数据集受托下载与技术交付服务。

提供高速下载与技术交付服务(收技术服务费,非数据销售)

暂未开放

当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。

使用方式

数据集获取(Figshare)

命令行下载

curl -L -o "cjas_a_1558186_sm3046.pdf" "https://ndownloader.figshare.com/files/15428711"
curl -L -o "cjas_a_1558186_sm9724.r" "https://ndownloader.figshare.com/files/13849418"

文件清单

  • cjas_a_1558186_sm3046.pdf(64.96 KB)
  • cjas_a_1558186_sm9724.r(28.71 KB)

文件总体积 93.66 KB(源站 API 实测)。

数据加载示例(图像类)

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Selection of treatments to fit the specific needs for a certain patient is one major challenge in modern medicine. Personalized treatments rely on established patient–treatment interactions. In recent years, various statistical methods for the identification and estimation of interactions between relevant covariates and treatment were proposed. In this article, different available methods for detection and estimation of a covariate–treatment interaction for a time-to-event outcome, namely the standard Cox regression model assuming a linear interaction, the fractional polynomials approach for interaction, the modified outcome approach, the local partial-likelihood approach, and STEPP (Subpopulation Treatment Effect Pattern Plots) were applied to data from the SPACE trial, a randomized clinical trial comparing stent-protected angioplasty (CAS) to carotid endarterectomy (CEA) in patients with symptomatic stenosis, with the aim to analyse the interaction between age and treatment. Time from primary intervention to the first relevant event (any stroke or death) was considered as outcome parameter. The analyses suggest a qualitative interaction between patient age and treatment indicating a lower risk after treatment with CAS compared to CEA for younger patients, while for elderly patients a lower risk after CEA was observed. Differences in the statistical methods regarding the observed results, applicability, and interpretation are discussed.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.7477574.v2(source: https://tandf.figshare.com/articles/dataset/Investigation_of_age_treatment_interaction_in_the_SPACE_trial_using_different_statistical_approaches/7477574)。

数据缺失?

依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。

⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。

获取专属数据定制方案