观察性研究中根据两个标准匹配一个样本

数据集获取（Figshare）

• 源站标题：Matching One Sample According to Two Criteria in Observational Studies
• DOI：10.6084/m9.figshare.16632580.v2
• 发布日期：2021-11-17
• 文件数：3 个
• 文件总体积：885.88 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Matching_One_Sample_According_to_Two_Criteria_in_Observational_Studies/16632580

命令行下载

curl -L -o "Appendix I.pdf" "https://ndownloader.figshare.com/files/31506605"
curl -L -o "Appendix II.pdf" "https://ndownloader.figshare.com/files/31506608"
curl -L -o "match2C_0.1.0.zip" "https://ndownloader.figshare.com/files/31506611"

文件清单

• Appendix I.pdf（144.42 KB）
• Appendix II.pdf（132.91 KB）
• match2C_0.1.0.zip（608.55 KB）

文件总体积 885.88 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

Multivariate matching has two goals (i) to construct treated and control groups that have similar distributions of observed covariates, and (ii) to produce matched pairs or sets that are homogeneous in a few key covariates. When there are only a few binary covariates, both goals may be achieved by matching exactly for these few covariates. Commonly, however, there are many covariates, so goals (i) and (ii) come apart, and must be achieved by different means. As is also true in a randomized experiment, similar distributions can be achieved for a high-dimensional covariate, but close pairs can be achieved for only a few covariates. We introduce a new polynomial-time method for achieving both goals that substantially generalizes several existing methods; in particular, it can minimize the earthmover distance between two marginal distributions. The method involves minimum cost flow optimization in a network built around a tripartite graph, unlike the usual network built around a bipartite graph. In the tripartite graph, treated subjects appear twice, on the far left and the far right, with controls sandwiched between them, and efforts to balance covariates are represented on the right, while efforts to find close individual pairs are represented on the left. In this way, the two efforts may be pursued simultaneously without conflict. The method is applied to our on-going study in the Medicare population of the relationship between superior nursing and sepsis mortality. The match2C package in R implements the method. 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.16632580.v2（source: https://tandf.figshare.com/articles/dataset/Matching_One_Sample_According_to_Two_Criteria_in_Observational_Studies/16632580）。