存在定性协变量-治疗交互作用时治疗分层变化点的置信区间估计数据集

数据集获取（Figshare）

• 源站标题：Dataset for: Confidence interval estimation for the changepoint of treatment stratification in the presence of a qualitative covariate-treatment interaction
• DOI：10.6084/m9.figshare.9962015.v1
• 发布日期：2019-11-08
• 文件数：3 个
• 文件总体积：25.82 KB
• 源站页面：https://wiley.figshare.com/articles/dataset/Dataset_for_Confidence_interval_estimation_for_the_changepoint_of_treatment_stratification_in_the_presence_of_a_qualitative_covariate-treatment_interaction/9962015

命令行下载

curl -L -o "Model_I.R" "https://ndownloader.figshare.com/files/17943566"
curl -L -o "Model_II.R" "https://ndownloader.figshare.com/files/17943569"
curl -L -o "Model_III.R" "https://ndownloader.figshare.com/files/17943572"

文件清单

• Model_I.R（11.38 KB）
• Model_II.R（6.95 KB）
• Model_III.R（7.50 KB）

文件总体积 25.82 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

The goal in stratified medicine is to administer the ‘best’ treatment to a patient. Not all patients might benefit from the same treatment, the choice of best treatment can depend on certain patient characteristics. In this article, it is assumed that a time-to-event outcome is considered as a patient-relevant outcome and a qualitative interaction between a continuous covariate and treatment exists, i.e. that patients with different values of one specific covariate should be treated differently. We suggest and investigate different methods for confidence interval estimation for the covariate value, where the treatment recommendation should be changed based on data collected in a randomized clinical trial. An adaptation of Fieller’s theorem, the delta method and different bootstrap approaches (normal, percentile-based, wild bootstrap) are investigated and compared in a simulation study. Extensions to multivariable problems are presented and evaluated.We observed appropriate confidence interval coverage following Fieller’s theorem irrespective of sample size, but at the cost of very wide or even infinite confidence intervals. The delta method and the
wild bootstrap approach provided the smallest intervals, but inadequate coverage for small to moderate event numbers, also depending on the location of the true changepoint. For the percentile-based bootstrap, wide intervals were observed and it was
slightly conservative regarding coverage, whereas the normal bootstrap did not provide acceptable results for many scenarios. The described methods were also applied to data from a randomized clinical trial comparing two treatments for patients with
symptomatic, severe carotid artery stenosis, considering patient’s age as predictive marker.

许可

CC0

引用

学术使用请引用 DOI 10.6084/m9.figshare.9962015.v1（source: https://wiley.figshare.com/articles/dataset/Dataset_for_Confidence_interval_estimation_for_the_changepoint_of_treatment_stratification_in_the_presence_of_a_qualitative_covariate-treatment_interaction/9962015）。