发表机构
University of Padova（帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该模拟研究基于观察数据，探讨纵向因果推断中治疗持续性对估计量性能的驱动作用，发现治疗持续性是影响估计量表现的关键因素，为解读估计量性能提供了参考。
AI 中文摘要
纵向临床数据日益丰富，为研究随时间变化的治疗效应提供了机会，但也带来了时变混杂和治疗决策演变的挑战。我们研究了当基线方法与纵向方法针对不同因果估计量时，纵向治疗动态如何影响因果效应估计。采用结构因果模型（SCM），我们在结合功能复杂性和治疗持续性的9种场景下，模拟了具有时变混杂、二元治疗和吸收性二元结局的数据。我们比较了3种基线估计量和2种纵向估计量在持续治疗方案下与蒙特卡洛真实值风险比（RR）的表现。结果表明，治疗持续性是估计量行为的主要驱动因素：高持续性会减小基线估计量与持续方案估计量之间的差异，使基线逆概率加权（IPTW）、目标最大似然估计（TMLE）更接近持续方案真实值，基线IPTW/TMLE的平均相对偏差从低持续性下的61%降至高持续性下的10%；而低持续性会引发实际的正性挑战，并增加纵向估计量的变异性，从高持续性到低持续性，纵向IPTW的经验95%区间宽度从0.22增至0.46，纵向目标最大似然估计（LTMLE）则从0.20增至0.36。这些发现强调，估计量性能应结合目标干预和生成观察数据的治疗过程进行解读。
英文摘要
Longitudinal clinical data are increasingly available, offering opportunities to study treatment effects over time but also raising challenges related to time-varying confounding and evolving treatment decisions. We investigate how longitudinal treatment dynamics affect causal effect estimation when baseline and longitudinal methods target different causal estimands. Using a structural causal model (SCM), we simulate data with time-varying confounding, binary treatment, and an absorbing binary outcome under 9 scenarios combining functional complexity and treatment persistence. We compare three baseline and two longitudinal estimators against Monte Carlo ground-truth risk ratios (RRs) under sustained treatment regimes. Results show that treatment persistence is the main driver of estimator behaviour. High persistence reduces the discrepancy between baseline and sustained-regime estimands, making baseline estimators closer to the sustained-regime ground truth (average relative deviation of baseline IPTW/TMLE decreasing from 61% under low persistence to 10% under high persistence), whereas low persistence induces practical positivity challenges and increases the variability of longitudinal estimators, with empirical 95% interval widths increasing from 0.22 to 0.46 for longitudinal IPTW and from 0.20 to 0.36 for LTMLE when moving from high to low persistence. These findings emphasise that estimator performance should be interpreted jointly with the target intervention and the treatment process generating the observed data.
Comments21st conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), September 2-4th 2026, Rome (Italy)
