Addressing missing data in the estimation of time-varying treatments in comparative effectiveness research.
Addressing missing data in the estimation of time-varying treatments in comparative effectiveness research.
复制标题
解决比较有效性研究中时变治疗估计中的缺失数据。
DOI：
10.1002/sim.9899
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发表时间：
2023
影响因子：
2
通讯作者：
Segura-Buisan J
中科院分区：
文献类型：
--
作者：
Segura-Buisan J
比较有效性研究通常关注评估随时间持续的治疗策略，即随时间变化的治疗。逆概率加权（IPW）通常用于通过根据每个时间点接受治疗的概率重新加权样本来解决时变混杂。IPW还可以用于解决任何缺失的数据，根据观察数据的概率重新加权个体。这两组不同权重的组合可能会导致治疗效果的估计效率低下，因为总权重可能存在高度可变性。或者，可以使用多重插补（MI）来解决缺失数据，方法是将每个缺失观察值替换为一组从缺失数据的后验预测分布（给定观察数据）中得出的合理值。最近的研究比较了IPW和MI，以解决随时间变化的治疗评价中的缺失数据，但他们关注的是缺失混杂因素和单调缺失数据模式。本文评估了MI和IPW在解决随时间推移测量的结局和混杂因素的缺失数据方面的相对优势，以及在单调和非单调缺失数据设置中的相对优势。通过一个全面的模拟研究，我们发现，MI始终提供低偏差和更精确的估计相比，IPW在广泛的情况。我们使用美国国家风湿病数据库说明了严重类风湿性关节炎患者生物药物评价方法选择的意义，其中25%的参与者缺失健康结局或随时间变化的混杂因素。
Comparative effectiveness research is often concerned with evaluating treatment strategies sustained over time, that is, time‐varying treatments. Inverse probability weighting (IPW) is often used to address the time‐varying confounding by re‐weighting the sample according to the probability of treatment receipt at each time point. IPW can also be used to address any missing data by re‐weighting individuals according to the probability of observing the data. The combination of these two distinct sets of weights may lead to inefficient estimates of treatment effects due to potentially highly variable total weights. Alternatively, multiple imputation (MI) can be used to address the missing data by replacing each missing observation with a set of plausible values drawn from the posterior predictive distribution of the missing data given the observed data. Recent studies have compared IPW and MI for addressing the missing data in the evaluation of time‐varying treatments, but they focused on missing confounders and monotone missing data patterns. This article assesses the relative advantages of MI and IPW to address missing data in both outcomes and confounders measured over time, and across monotone and non‐monotone missing data settings. Through a comprehensive simulation study, we find that MI consistently provided low bias and more precise estimates compared to IPW across a wide range of scenarios. We illustrate the implications of method choice in an evaluation of biologic drugs for patients with severe rheumatoid arthritis, using the US National Databank for Rheumatic Diseases, in which 25% of participants had missing health outcomes or time‐varying confounders.