Penalized Spline of Propensity Methods for Treatment Comparison
多中心艾滋病队列研究的表格数据，用于评估抗逆转录病毒治疗对CD4计数的影响，采用PENCOMP方法进行因果推断。
基本信息
资源简介
Penalized Spline of Propensity Methods for Treatment Comparison
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 3 个文件、合计 51.61 MB，主题涵盖 Causal inference、Double robustness、Time-dependent confounders、Rubin’s causal model，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
配套论文《Penalized Spline of Propensity Methods for Treatment Comparison》发表于 Journal of the American Statistical Association（2019），DOI：10.1080/01621459.2018.1518234。源站首次发布：2019-04-19。
【数据内容】
源站原始描述：Valid causal inference from observational studies requires controlling for confounders. When time-dependent confounders are present that serve as mediators of treatment effects and affect future treatment assignment, standard regression methods for controlling for confounders fail. Similar issues also arise in trials with sequential randomization, when randomization at later time points is based on intermediate outcomes from earlier randomized assignments. We propose a robust multiple imputation…
【数据结构与技术规格】
文件数 3 个，合计 51.61 MB。Appendix.pdf：application/pdf，266.04 KB。JASA_ACS_author_form_little.pdf：application/pdf，24.61 KB。数据集 DOI：10.6084/m9.figshare.8016437.v1。
【主题与分类】
源站学科分类：Biochemistry、Medicine、Genetics、Molecular Biology、Pharmacology、Biotechnology。主题标签：Causal inference、Double robustness、Time-dependent confounders、Rubin’s causal model。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/Penalized_Spline_of_Propensity_Methods_for_Treatment_Comparison/8016437
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Penalized Spline of Propensity Methods for Treatment Comparison. Taylor & Francis. Zhou, Tingting; Elliott, Michael R.; Little, Roderick J. A
【使用注意】
· 源站最后修改于 2024-02-12，引用时请注意版本时效。
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Penalized Spline of Propensity Methods for Treatment Comparison
- DOI：10.6084/m9.figshare.8016437.v1
- 发布日期：2019-04-19
- 文件数：3 个
- 文件总体积：51.61 MB
- 源站页面：https://tandf.figshare.com/articles/dataset/Penalized_Spline_of_Propensity_Methods_for_Treatment_Comparison/8016437
命令行下载
curl -L -o "Appendix.pdf" "https://ndownloader.figshare.com/files/14934848"
curl -L -o "JASA_ACS_author_form_little.pdf" "https://ndownloader.figshare.com/files/14934851"
curl -L -o "codes.zip" "https://ndownloader.figshare.com/files/14934854"
文件清单
Appendix.pdf（266.04 KB）JASA_ACS_author_form_little.pdf（24.61 KB）codes.zip（51.32 MB）
文件总体积 51.61 MB（源站 API 实测）。
数据说明
Valid causal inference from observational studies requires controlling for confounders. When time-dependent confounders are present that serve as mediators of treatment effects and affect future treatment assignment, standard regression methods for controlling for confounders fail. Similar issues also arise in trials with sequential randomization, when randomization at later time points is based on intermediate outcomes from earlier randomized assignments. We propose a robust multiple imputation-based approach to causal inference in this setting called penalized spline of propensity methods for treatment comparison (PENCOMP), which builds on the penalized spline of propensity prediction method for missing data problems. PENCOMP estimates causal effects by imputing missing potential outcomes with flexible spline models and draws inference based on imputed and observed outcomes. Under the SUTVA, positivity, and ignorability assumptions, PENCOMP has a double robustness property for causal effects. Simulations suggest that it tends to outperform doubly robust marginal structural modeling when the weights are variable. We apply our method to the multicenter AIDS cohort study to estimate the effect of antiretroviral treatment on CD4 counts in HIV-infected patients. Supplementary materials for this article are available online. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.8016437.v1（source: https://tandf.figshare.com/articles/dataset/Penalized_Spline_of_Propensity_Methods_for_Treatment_Comparison/8016437）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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