NIHR129713 - 林奇综合征女性妇科监测的效果与成本效益 - 经济评估模拟结果 - 数据文件

数据集获取（Figshare）

• 源站标题：NIHR129713 - Effectiveness and cost-effectiveness of gynaecological surveillance for women with Lynch syndrome - Economic evaluation simulation results - Data files
• DOI：10.6084/m9.figshare.20496654.v1
• 发布日期：2023-01-16
• 文件数：5 个
• 文件总体积：8.71 GB
• 源站页面：https://figshare.com/articles/dataset/NIHR129713_-_Effectiveness_and_cost-effectiveness_of_gynaecological_surveillance_for_women_with_Lynch_syndrome_-_Economic_evaluation_simulation_results_-_Data_files/20496654

命令行下载

curl -L -o "cancer-free-survival.tar" "https://ndownloader.figshare.com/files/38854272"
curl -L -o "cancer-survival.tar" "https://ndownloader.figshare.com/files/38854407"
curl -L -o "patient-level-outcomes.tar" "https://ndownloader.figshare.com/files/38854434"
curl -L -o "cancer-outcomes.tar" "https://ndownloader.figshare.com/files/38854437"
curl -L -o "params.tar" "https://ndownloader.figshare.com/files/38854440"

文件清单

• cancer-free-survival.tar（2.94 GB）
• cancer-survival.tar（3.31 GB）
• patient-level-outcomes.tar（2.42 GB）
• cancer-outcomes.tar（19.56 MB）
• params.tar（12.31 MB）

文件总体积 8.71 GB（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 results are contained in 5 datasets, each of which is described in further detail below. Each dataset is stored as a tar file, which when extracted will be in Hive/Feather format as:
{dataset}/age={age}/genotype={genotype}/data.feather
Where {age} is the starting age of the simulated individuals (30, 35, 40 or 45) and {genotype} is the genotype of the simulated individuals (path_MLH1, path_MSH2, path_MSH6, path_PMS2).
Each data.feather file covers 500 different parameter sets, and for each of these 4 competing options, and for each of these 1000 simulated individuals, i.e., 2 million simulated individuals per data.feather and 32 million simulated individuals overall.

patient-level-outcomes

This gives patient-level costs, life years and QALYs for conducting the economic evaluation. Each row/observation corresponds to a single simulated patient.
Fields:

• params_uuid - uniquely identifies the parameter set (can be used to join with other datasets)

• individual_uuid - uniquely identifies the individual (can be used to join with other datasets)

• competing_option - text description for which of the four competing options applied to the individual

• total_costs - total costs over the lifetime for the individual, discounted at the rate params[‘‘analysis.discount_rate.cost’’] (constant 3.5% in the current version)

• total_life_years - total life years for the individual, discounted at the rate params[‘‘analysis.discount_rate.ly’’] (constant 3.5% in the current version)

• total_qalys - total quality-adjusted life years for the individual, discounted at the rate params[‘‘analysis.discount_rate.qaly’’] (constant 3.5% in the current version)

Tip: As each observation corresponds to a single individual it is possible to calculate undiscounted life years lived from total_life_years:
drc = log(1 + params[‘‘analysis.discount_rate.ly’’])total_life_years_undiscounted = - log(1 - drc * total_life_years) / drc

params

The parameters for the simulations. Each row/observation corresponds to a single parameter set.

• params_uuid - uniquely identifies the parameter set (can be used to join with other datasets)

• … - parameter values, which are mostly scalars b

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.20496654.v1（source: https://figshare.com/articles/dataset/NIHR129713_-_Effectiveness_and_cost-effectiveness_of_gynaecological_surveillance_for_women_with_Lynch_syndrome_-_Economic_evaluation_simulation_results_-_Data_files/20496654）。