严重脓毒症死亡率观察性研究中的离散优化与随机化推断数据集

数据集获取（Figshare）

• 源站标题：Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality
• DOI：10.6084/m9.figshare.1627937.v1
• 发布日期：2015-12-23
• 文件数：3 个
• 文件总体积：360.43 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Discrete_Optimization_for_Interpretable_Study_Populations_and_Randomization_Inference_in_an_Observational_Study_of_Severe_Sepsis_Mortality/1627937

命令行下载

curl -L -o "final_supplement.bbl" "https://ndownloader.figshare.com/files/2613330"
curl -L -o "final_supplement.pdf" "https://ndownloader.figshare.com/files/2613329"
curl -L -o "final_supplement.tex" "https://ndownloader.figshare.com/files/2613331"

文件清单

• final_supplement.bbl（2.14 KB）
• final_supplement.pdf（324.49 KB）
• final_supplement.tex（33.80 KB）

文件总体积 360.43 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

Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference to form confidence intervals for the average treatment effect with binary outcomes. Our solution to problem (1) incorporates existing suggestions in the literature while yielding a study population that is easily understood in terms of the covariates themselves, and can be solved using an efficient branch-and-bound algorithm. We address problem (2) by solving a linear integer program to use the worst-case variance of the average treatment effect among values for unobserved potential outcomes that are compatible with the null hypothesis. Our analysis finds no evidence for a difference between the 60-day mortality rates if all individuals were admitted to the ICU and if all patients were admitted to the hospital ward among less severely ill patients and among patients with cryptic septic shock. We implement our methodology in R, providing scripts in the supplementary material.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.1627937.v1（source: https://tandf.figshare.com/articles/dataset/Discrete_Optimization_for_Interpretable_Study_Populations_and_Randomization_Inference_in_an_Observational_Study_of_Severe_Sepsis_Mortality/1627937）。