通过隐含预后因子推断与平衡，提升真实世界生存数据中的治疗效应估计精度。
Observing the unobserved confounding through its effects: toward randomized trial-like estimates from real-world survival data
- 从相似患者间生存差异反推未观测的预后因子，构建隐含协变量U。
- 在三组观察队列中，使用U平衡后对齐随机试验的危险比，误差降低十倍。
- 适用于需从真实世界数据获取类随机对照试验结果的研究者。
随机对照试验成本高、耗时长且常不可行，而观察性数据中的治疗效应估计受限于未观测混杂因素。本文提出三步框架：首先，基于具有相似已知特征、相同治疗但结果不同的患者间限制均值生存时间（RMST）差异，推断一个潜在预后因子（U）；其次，利用预后匹配、熵平衡或逆概率加权法，使U与已知基线协变量平衡；最后，采用多变量生存分析估计风险比（HR）。在三组具随机对照试验基准的观察队列、两组随机对照试验队列及六组多中心观察队列中验证。结果显示，在三组观察队列（九次比较）中，平衡U后与试验HR一致性全部提升；最强情境下，绝对对数风险比误差相比仅用已知协变量降低约十倍（平均减少0.344，p=0.001）。在两组随机对照试验队列中，U在各组间已自然平衡（多数SMD<0.1），调整对对数风险比影响极小（平均绝对变化0.08）。在六组多中心队列中，按中心内平衡U可降低化疗风险比估计的跨中心差异（平均减少0.147，p=0.016）；当直接跨中心平衡人群构成以校正病例组合差异时，75%–100%的比较中缩小了跨中心生存差异。
原文摘要 · Abstract (English)
Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved confounding. Methods: We developed a three-step framework to address unobserved confounding in observational survival data. First, we infer a latent prognostic factor (U) from restricted mean survival time (RMST) discrepancies between patients with similar observed factors, the same treatment, and divergent outcomes, leveraging the idea that the aggregate effect of unmeasured factors can be inferred even if individual factors cannot. Second, we balance U with observed baseline covariates using prognostic matching, entropy balancing, or inverse probability of treatment weighting. Third, we apply multivariable survival analysis to estimate hazard ratios (HRs). We evaluated the framework in three observational cohorts with RCT benchmarks, two RCT cohorts, and six multicenter observational cohorts. Results: In three observational cohorts (nine comparisons), balancing U improved agreement with trial HRs in all cases; in the strongest settings, it reduced absolute log-HR error by approximately ten-fold versus using observed covariates alone (mean reduction 0.344; p=0.001). In two RCT cohorts, U was balanced across arms (most SMDs <0.1) and adjustment had minimal impact on log-HRs (mean absolute change 0.08). Across six multicenter cohorts, balancing U within centers reduced cross-center dispersion in chemotherapy log-HR estimates (mean reduction 0.147; p=0.016); when populations were directly balanced across centers to account for case-mix differences, cross-center survival differences were narrowed in 75%-100% of comparisons. Conclusions: Inferring and balancing a latent prognostic signal may reduce unobserved confounding and improve treatment-effect estimation from real-world data.
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