附录：在多种治疗方案比较有效性研究中可视化目标估计量

数据集获取（Figshare）

• 源站标题：Appendix: Visualizing the target estimand in comparative effectiveness studies with multiple treatments
• DOI：10.6084/m9.figshare.25071107.v1
• 发布日期：2024-02-05
• 文件数：1 个
• 文件总体积：908.88 KB
• 源站页面：https://becaris.figshare.com/articles/dataset/Appendix_Visualizing_the_target_estimand_in_comparative_effectiveness_studies_with_multiple_treatments/25071107

命令行下载

curl -L -o "appendix.docx" "https://ndownloader.figshare.com/files/44240336"

文件清单

• appendix.docx（908.88 KB）

文件总体积 908.88 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

These are peer-reviewed supplementary materials for the article ‘‘Visualizing the target estimand in comparative effectiveness studies with multiple treatments’’ published in the Journal of Comparative Effectiveness Research.

• Data-generating mechanism
• Additional results
• Additional Multiple Sclerosis Case Study Methods and Results
• Additional methods
• Cohort Definition
• Details on baseline covariates
• Missing data imputation
• Matching
• Additional results
• Missing data pattern
• Baseline Characteristics Before Imputation
• Baseline characteristics after imputation
• Additional bivariate ellipses
• Treatment effect heterogeneity exploration
• Assessment of matching implementation
• Joy plots
• References
  Aim: Comparative effectiveness research using real-world data often involves pairwise propensity score matching to adjust for confounding bias. We show that corresponding treatment effect estimates may have limited external validity, and propose two visualization tools to clarify the target estimand. Materials & methods: We conduct a simulation study to demonstrate, with bivariate ellipses and joy plots, that differences in covariate distributions across treatment groups may affect the external validity of treatment effect estimates. We showcase how these visualization tools can facilitate the interpretation of target estimands in a case study comparing the effectiveness of teriflunomide (TERI), dimethyl fumarate (DMF) and natalizumab (NAT) on manual dexterity in patients with multiple sclerosis. Results: In the simulation study, estimates of the treatment effect greatly differed depending on the target population. For example, when comparing treatment B with C, the estimated treatment effect (and respective standard error) varied from -0.27 (0.03) to -0.37 (0.04) in the type of patients initially receiving treatment B and C, respectively. Visualization of the matched samples revealed that covariate distributions vary for each comparison and cannot be used to target one common treatment effect for the three treatment comparisons. In the case study, the bivariate distribution of age and disease duration varied across the population of patients receiving

许可

CC BY-NC-ND 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.25071107.v1（source: https://becaris.figshare.com/articles/dataset/Appendix_Visualizing_the_target_estimand_in_comparative_effectiveness_studies_with_multiple_treatments/25071107）。