In studies of time-to-event outcomes with unmeasured heterogeneity, the hazard ratio for treatment is known to have a complex causal interpretation. Accelerated failure time (AFT) models, which assess the effect on the survival time ratio scale, are often suggested as a better alternative because they model a parameter with direct causal interpretation while allowing straightforward adjustment for measured confounders. In this work, we formalize the causal interpretation of the acceleration factor in AFT models using structural causal models and data under independent censoring. We prove that the acceleration factor is a valid causal effect measure, even in the presence of frailty and treatment effect heterogeneity. Through simulations, we show that the acceleration factor better captures the causal effect than the hazard ratio when both AFT and conditional proportional hazards models apply. Additionally, we extend the interpretation to systems with time-dependent acceleration factors, illustrating the impossibility of distinguishing between a time-varying homogeneous effect and unmeasured effect heterogeneity. While the causal interpretation of acceleration factors is promising, we caution practitioners about potential challenges for the interpretation in the presence of effect heterogeneity.

翻译：在存在未测量异质性的时间-事件结局研究中，已知治疗的风险比具有复杂的因果解释。加速失效时间模型通过评估生存时间比尺度上的效应，常被视为更优替代方案，因为它对具有直接因果解释的参数进行建模，同时允许对已测量的混杂因素进行直接调整。本研究利用结构因果模型和独立删失下的数据，形式化地阐述了AFT模型中加速因子的因果解释。我们证明，即使在存在脆弱性和治疗效果异质性的情况下，加速因子仍是一个有效的因果效应度量。通过模拟实验，我们发现在AFT模型和条件比例风险模型均适用时，加速因子比风险比更能准确捕捉因果效应。此外，我们将该解释扩展到具有时变加速因子的系统，阐明时变同质效应与未测量效应异质性之间无法区分的特性。尽管加速因子的因果解释前景广阔，我们仍提醒实践者注意在存在效应异质性时可能面临的解释挑战。