大规模生存数据中时变效应建模数据集

数据集获取（Figshare）

• 源站标题：Modeling Time-Varying Effects With Large-Scale Survival Data: An Efficient Quasi-Newton Approach
• DOI：10.6084/m9.figshare.3850506.v3
• 发布日期：2019-10-25
• 文件数：1 个
• 文件总体积：2.03 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Modeling_Time-varying_Effects_with_Large-scale_Survival_Data_An_Efficient_Quasi-Newton_Approach/3850506

命令行下载

curl -L -o "ucgs_a_1237364_sm0794.zip" "https://ndownloader.figshare.com/files/18112499"

文件清单

• ucgs_a_1237364_sm0794.zip（2.03 MB）

文件总体积 2.03 MB（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

Nonproportional hazards models often arise in biomedical studies, as evidenced by a recent national kidney transplant study. During the follow-up, the effects of baseline risk factors, such as patients’ comorbidity conditions collected at transplantation, may vary over time. To model such dynamic changes of covariate effects, time-varying survival models have emerged as powerful tools. However, traditional methods of fitting time-varying effects survival model rely on an expansion of the original dataset in a repeated measurement format, which, even with a moderate sample size, leads to an extremely large working dataset. Consequently, the computational burden increases quickly as the sample size grows, and analyses of a large dataset such as our motivating example defy any existing statistical methods and software. We propose a novel application of quasi-Newton iteration method to model time-varying effects in survival analysis. We show that the algorithm converges superlinearly and is computationally efficient for large-scale datasets. We apply the proposed methods, via a stratified procedure, to analyze the national kidney transplant data and study the impact of potential risk factors on post-transplant survival. Supplementary materials for this article are available online.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.3850506.v3（source: https://tandf.figshare.com/articles/dataset/Modeling_Time-varying_Effects_with_Large-scale_Survival_Data_An_Efficient_Quasi-Newton_Approach/3850506）。