Considerations for Estimating Causal Effects of Informatively Timed Treatments
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具有信息性处理时点的因果效应估计
- 研究问题
- 当连续处理决策的时间因人而异，且等待时间关联后续处理与潜在生存结果时，如何避免忽略时点信息造成的因果估计偏差？
- 方法与思路
- 作者将相邻处理决策之间的等待时间视为时变混杂因素，使用 g 方法调整，并讨论期间死亡、删失及离散与连续时间建模。
- 材料陈述的贡献
- 摘要称形式化信息性时点问题，用合成示例展示未调整等待时间的偏差，并提供调整方案、识别讨论及公开软件实施指导。
- 阅读关注点
- 应核对等待时间纳入历史信息的方式、各模型下的识别条件，以及死亡和删失如何进入处理序列、风险集与估计步骤。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未完整列出识别与删失假设，合成示例不能证明所有信息性时点均可校正。
原文摘要
Epidemiological studies are often concerned with estimating causal effects of a sequence of treatment decisions on survival outcomes. In many settings, treatment decisions do not occur at fixed, pre-specified followup times. Rather, timing varies across subjects in ways that may be informative of subsequent treatment decisions and potential outcomes. Awareness of the issue and its potential solutions is lacking in the literature, which motivate this work. Here, we formalize the issue of informative timing, problems associated with ignoring it, and show how g-methods can be used to analyze sequential treatments that are informatively timed. As we describe, in such settings, the waiting times between successive treatment decisions may be properly viewed as a time-varying confounders. Using synthetic examples, we illustrate how g-methods that do not adjust for these waiting times may be biased and how adjustment can be done in scenarios where patients may die or be censored in between treatments. We draw connections between adjustment and identification with discrete-time versus continuous-time models. Finally, we provide implementation guidance and examples using publicly available software. Our concluding message is that 1) considering timing is important for valid inference and 2) correcting for informative timing can be done with g-methods that adjust for waiting times between treatments as time-varying confounders.