Irregular measurement times in estimating time-varying treatment effects: Categorizing biases and comparing adjustment methods
时变处理效应估计中的不规则测量时点：偏差分类与调整方法比较AI 解读 ↓
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时变处理效应估计中的不规则测量时点：偏差分类与调整方法比较
- 研究问题
- 协变量测量时点不规则时，会通过哪些路径影响时变处理的因果效应估计，现有两类重加权方法分别能处理哪些混杂和选择偏差？
- 方法与思路
- 作者梳理测量时点引起偏差的后门路径，将混杂分为直接、经测量变量和经未测量变量三类，并用模拟及 pre-DIVA 数据比较调整方法。
- 材料陈述的贡献
- 摘要称，将时间作为混杂变量与按测量时点重加权可处理不同偏差类别，某些情形需组合使用，并提出帮助选择分析方法的分类框架。
- 阅读关注点
- 应核对各偏差类别的因果图、测量机制与调整假设，重点区分选择偏差和混杂，检查两种权重及其组合在不同情形下的有效性。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收。摘要未展示具体路径和识别条件，不能认为任一重加权方案普遍消除偏差。
原文摘要
To estimate the causal effect of treatments that vary over time from observational data, one must adjust for time-varying confounding. A common procedure to address confounding is the use of inverse probability of treatment weighting methods. However, the timing of covariate measurements is often irregular, which may introduce additional confounding bias as well as selection bias into the causal effect estimate. Two reweighting methods have been proposed to adjust for these biases: time-as-confounder and reweighting by measurement time. However, it is currently not well understood in which situations these irregularly timed measurements induce bias, and how the available reweighting methods compare to each other in different situations. In this work, we provide a complete inventarization of all possible backdoor paths through which bias is induced. Based on these paths, we distinguish three categories of confounding bias by measurement time: direct confounding (DC), confounding through measured variables (CMV), and confounding through unmeasured variables (CUV). These categories differ in the assumptions and reweighting methods necessary to adjust for bias and may occur simultaneously with selection bias. Through simulation studies, we illustrate: 1. Reweighting by measurement time may be used to adjust for selection bias and confounding through measured variables; 2. Time-as-confounder may be used to adjust for all categories of confounding bias, but not selection bias; 3. In some cases, the use of a combination of both techniques may be used to adjust for both confounding and selection bias. We finally apply the categorization and reweighting methods on the pre-DIVA data set. Adjusting for measurement times is crucial in order to avoid bias, and the categorization of biases and techniques that we introduce may help researchers to choose the appropriate analysis.