Can we detect treatment effect waning from time-to-event data?
能否从事件时间数据中识别处理效应衰减AI 解读 ↓
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能否从事件时间数据中识别处理效应衰减
- 研究问题
- 即使在随机试验中，能否从风险函数、生存曲线或因果风险比判断处理保护作用随时间衰减，还是需要额外不可检验假设？
- 方法与思路
- 作者分析风险比较的选择偏差，讨论主层分层和受控直接效应定义的因果风险比及其界限，并比较相同生存曲线下的不同情景。
- 材料陈述的贡献
- 摘要称，因果风险比趋近一不必然表示保护作用衰减；同一生存函数可对应有无衰减两种情景，说明标准数据的识别局限。
- 阅读关注点
- 核对处理效应衰减的形式定义、反例构造、因果风险比所需假设及界限，并区分累积生存效应与随时间变化的保护作用。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出模型假设和界限公式，不能从风险比走势直接推断真实效应衰减。
原文摘要
Understanding how the causal effect of a treatment evolves over time, including the potential for waning, is important for informed decisions on treatment discontinuation or repetition. For example, waning vaccine protection influences booster dose recommendations, while cost-effectiveness analyses require accounting for long-term efficacy of treatments. However, there is no consensus on the methodology to assess and account for treatment effect waning. Even in randomized controlled trials, the common naïve comparison of hazard functions can lead to misleading causal conclusions due to inherent selection bias. Although comparing survival curves is sometimes recommended as a safer measure of causal effect, it only represents a cumulative effect over time and does not address treatment effect waning. We also explore recent formulations of causal hazard ratios, based on the principal stratification approach or the controlled direct effect. These causal hazard ratios cannot be identified without strong modeling assumptions, but bounds can be derived accounting for unobserved heterogeneity and one could try to use them to detect treatment effect waning. However, we illustrate that an increase in causal hazard ratios towards one does not necessarily mean that the protective effect of the treatment is fading. Furthermore, the same survival functions may correspond to both scenarios with and without waning, which shows that treatment effect waning cannot be identified from standard time-to-event data without strong untestable modeling assumptions.