Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models
纵向观察数据中的生存因果推断：序贯试验与边际结构模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究时变治疗的生存因果效应，比较目标试验模拟、逆概率加权与边际结构模型，明确对应因果推断。
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纵向观察数据中的生存因果推断：序贯试验与边际结构模型
- 研究问题
- 利用纵向患者观察数据估计时变治疗对生存的因果影响时，序贯试验与加权边际结构模型如何识别相同的边际风险差？
- 方法与思路
- 作者将未治疗且符合条件者组成连续试验，对偏离初始治疗状态者删失并加权，随后合并试验，与治疗概率加权方法比较。
- 材料陈述的贡献
- 摘要称，两种方法可估计相同边际风险差；模拟中序贯试验多数随访时点效率更高，但部分情形的晚期比较可能反转。
- 阅读关注点
- 应核对两种方法的目标参数、时变混杂假设、删失与治疗权重构造，以及重复入组的数据使用和晚期效率反转原因。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收。摘要未列出完整识别条件，模拟效率结论依赖情景与随访时点。
原文摘要
Longitudinal observational patient data can be used to investigate the causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for controlling for the time-dependent confounding that typically occurs. The most commonly used is inverse probability weighted estimation of marginal structural models (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, in particular in combination with the target trial emulation framework. This approach involves creating a sequence of `trials' from new time origins, restricting to individuals as yet untreated and meeting other eligibility criteria, and comparing treatment initiators and non-initiators. Individuals are censored when they deviate from their treatment status at the start of each `trial' (initiator/non-initiator) and this is addressed using inverse probability of censoring weights. The analysis is based on data combined across trials. We show that the sequential trials approach can estimate the parameter of a particular MSM, and compare it to a MSM-IPTW with respect to the estimands being identified, the assumptions needed and how data are used differently. We show how both approaches can estimate the same marginal risk differences. The two approaches are compared using a simulation study. The sequential trials approach, which tends to involve less extreme weights than MSM-IPTW, results in greater efficiency for estimating the marginal risk difference at most follow-up times, but this can, in certain scenarios, be reversed at late time points. We apply the methods to longitudinal observational data from the UK Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.