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.

翻译：合成对照（SC）方法近年来在经济学领域迅速普及，其应用场景通常基于线性输入-输出假设，推断标准连续结局变量的处理效应。然而在医学应用中，生存结局往往成为主要关注目标，这种设定下常用数据生成过程（DGPs）与目标参数均存在根本差异。本文因此探究在生存分析中，合成对照能否作为匹配方法的替代方案。研究发现：由于合成对照依赖线性假设，在常用生存数据生成过程中——即便考虑加速失效时间模型中其他尺度上的线性可能性——其对真实期望生存时间的估计通常存在偏倚。此外，合成对照单位服从方差低于真实对照单位的分布，导致其分布特征（如生存曲线）的汇总指标在许多生存分析中对目标参数存在偏倚。尽管如此，我们仍强调：当上述偏差小于不完美匹配所产生的外推偏差时，使用合成对照仍可改进匹配效果，并进一步探讨了通过正则化方法权衡两种方法缺陷的应用策略。