Is checking for sequential positivity violations getting you down? Try sPoRT!
用 sPoRT 检查序贯正值性违背AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接诊断纵向因果推断所需的序贯正值性，并涉及边际结构模型与动态处理策略。
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用 sPoRT 检查序贯正值性违背
- 研究问题
- 纵向因果分析中，如何减少对多个参数化倾向得分模型的依赖，识别可能违反序贯正值性的可解释人群子组？
- 方法与思路
- 作者提出序贯正值性回归树 sPoRT，针对静态或动态处理策略提供按时间分层和合并的版本，并以 HIV 治疗启动时机展示。
- 材料陈述的贡献
- 摘要称算法可识别正值性违背的子组及混杂变量模式，提供易解释的应用结果、实践建议和 R notebook，但未报告定量检验性能。
- 阅读关注点
- 核对违背判定阈值、回归树划分规则与样本量要求，区分结构性和有限样本重叠不足，并检查时间平滑对诊断结果的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明诊断误差或阈值选择，算法未检出违背不能据此证明序贯正值性成立。
原文摘要
Background: Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this assumption can be challenging because it usually relies on multiple parametric propensity score models, unlikely all correctly specified. Therefore, we propose a new algorithm, called sequential Positivity Regression Tree (sPoRT), to overcome this issue and identify the subgroups found to be violating this assumption, allowing for insights about the nature of the violations and potential solutions. Methods: We present different versions of sPoRT based on either stratifying or pooling over time under static or dynamic treatment strategies. This methodological development was motivated by a real-life application of the impact of the timing of initiation of HIV treatment with and without smoothing over time, which we also use to demonstrate the method. Results: The illustration of sPoRT demonstrates its easy use and the interpretability of the results for applied epidemiologists. Furthermore, an R notebook showing how to use sPoRT in practice is available at github.com/ArthurChatton/sPoRT-notebook. Conclusions: The sPoRT algorithm provides interpretable subgroups violating the sequential positivity violation, allowing patterns and trends in the confounders to be easily identified. We finally provided practical implications and recommendations when positivity violations are identified.