Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging due to the nonsmoothness of artificial censoring. We propose a class of continuous-time structural failure time models that respects the continuous-time nature of the underlying data processes. Under a martingale condition of no unmeasured confounding, we show that the model parameters are identifiable from a potentially infinite number of estimating equations. Using the semiparametric efficiency theory, we derive the first semiparametric doubly robust estimators, which are consistent if the model for the treatment process or the failure time model, but not necessarily both, is correctly specified. Moreover, we propose using inverse probability of censoring weighting to deal with dependent censoring. In contrast to artificial censoring, our weighting strategy does not introduce nonsmoothness in estimation and ensures that resampling methods can be used for inference.

中文翻译：

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连续时间过程中结构失效时间模型的半参数估计

结构失效时间模型是用于估计随时间变化的治疗对生存结果的影响的因果模型。已经提出了 G 估计和人工审查来估计存在时间相关混杂和行政审查的模型参数。然而，大多数现有方法需要手动将数据预处理为规则间隔的数据，这可能会使后续的因果分析无效。此外，由于人工审查的不平滑性，计算和推理具有挑战性。我们提出了一类连续时间结构故障时间模型，它尊重底层数据过程的连续时间性质。在没有不可测量混杂的鞅条件下，我们表明模型参数可以从潜在的无限数量的估计方程中识别出来。使用半参数效率理论，我们推导出第一个半参数双稳健估计量，如果正确指定了处理过程模型或故障时间模型（但不一定两者），则它们是一致的。此外，我们建议使用审查权重的逆概率来处理相关审查。与人工审查相比，我们的加权策略不会在估计中引入不平滑性，并确保重采样方法可用于推理。被正确指定。此外，我们建议使用审查权重的逆概率来处理相关审查。与人工审查相比，我们的加权策略不会在估计中引入不平滑性，并确保重采样方法可用于推理。被正确指定。此外，我们建议使用审查权重的逆概率来处理相关审查。与人工审查相比，我们的加权策略不会在估计中引入不平滑性，并确保重采样方法可用于推理。