生存分析中合成控制的警示故事
Cautionary Tales on Synthetic Controls in Survival Analyses
AI总结：
本文探讨合成控制（SC）方法在生存分析中替代匹配方法的适用性，发现其线性假设和低方差特性会导致期望生存时间和生存曲线产生偏差，但当外推偏差更大时SC仍有优势，正则化可权衡两者缺点。
摘要：
合成控制（SC）方法近期在经济学中迅速普及，被应用于在假设线性输入输出关系的情况下推断处理对标准连续结局的影响。相反，在医学应用中，生存结局通常是主要关注点，在这种设定下，通常假设的数据生成过程（DGP）和目标参数均有所不同。因此，本文探讨SC方法是否以及何时能作为生存分析中匹配方法的替代方案。我们发现，由于SC依赖线性假设，在通常假设的生存DGP中，即使考虑到如加速失效时间模型中在其他尺度上存在线性的可能性，它们对真实的期望生存时间通常也会产生偏差。此外，我们发现，由于SC单元遵循的分布方差低于真实对照单元，其分布的概括性指标（如生存曲线）在许多生存分析中会对目标参数产生偏差。尽管如此，我们也强调，当上述偏差超过不完美匹配所表现出的外推偏差时，使用SC仍可改进匹配方法，并研究了正则化的使用以权衡两种方法的缺点。
英文摘要：
Synthetic control (SC) methods have gained rapid popularity in economics recently, where they have been applied in the context of inferring the effects of treatments on standard continuous outcomes assuming linear input-output relations. In medical applications, conversely, survival outcomes are often of primary interest, a setup in which both commonly assumed data-generating processes (DGPs) and target parameters are different. In this paper, we therefore investigate whether and when SCs could serve as an alternative to matching methods in survival analyses. We find that, because SCs rely on a linearity assumption, they will generally be biased for the true expected survival time in commonly assumed survival DGPs -- even when taking into account the possibility of linearity on another scale as in accelerated failure time models. Additionally, we find that, because SC units follow distributions with lower variance than real control units, summaries of their distributions, such as survival curves, will be biased for the parameters of interest in many survival analyses. Nonetheless, we also highlight that using SCs can still improve upon matching whenever the biases described above are outweighed by extrapolation biases exhibited by imperfect matches, and investigate the use of regularization to trade off the shortcomings of both approaches.
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