Marginal structural models (MSMs) allow for causal analysis of longitudinal data. The standard MSM is based on discrete time models, but the continuous-time MSM is a conceptually appealing alternative for survival analysis. In applied analyses, it is often assumed that the theoretical treatment weights are known, but these weights are usually unknown and must be estimated from the data. Here we provide a sufficient condition for continuous-time MSM to be consistent even when the weights are estimated, and we show how additive hazard models can be used to estimate such weights. Our results suggest that continuous-time weights perform better than IPTW when the underlying process is continuous. Furthermore, we may wish to transform effect estimates of hazards to other scales that are easier to interpret causally. We show that a general transformation strategy can be used on weighted cumulative hazard estimates to obtain a range of other parameters in survival analysis, and explain how this strategy can be applied on data using our R packages ahw and transform.hazards.

中文翻译：

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附加危害估算器对于连续时间边际结构模型是一致的。

边际结构模型（MSM）允许对纵向数据进行因果分析。标准MSM基于离散时间模型，但是连续时间MSM在概念上是生存分析的理想选择。在应用分析中，通常假定理论治疗权重是已知的，但是这些权重通常是未知的，必须从数据中进行估算。在此，即使估算了权重，我们也为连续时间MSM保持一致提供了充分的条件，并展示了如何使用附加危害模型来估算此类权重。我们的结果表明，当基础流程是连续的时，连续时间权重比IPTW的效果更好。此外，我们可能希望将危害的影响估算值转换为其他因果关系更容易理解的尺度。R打包ahw和transform.hazards。