Nonparametric estimation of an optimal treatment rule with fused randomized trials and missing effect modifiers
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融合随机试验且效应修饰变量缺失时最优治疗规则的非参数估计
- 研究问题
- 多个随机试验研究共同治疗方案但测量的患者协变量不一致时，如何融合信息估计个体化治疗规则，使目标人群的结局得到优化？
- 方法与思路
- 作者提出跨随机试验融合信息的非参数最优治疗规则估计量，处理效应修饰变量测量不匹配，并应用于阿片类药物使用障碍的药物治疗试验。
- 材料陈述的贡献
- 摘要称，方法利用多个试验的信息估计最优动态治疗规则；应用旨在将患者亚组匹配到可降低恢复规律性阿片类药物使用风险的药物。
- 阅读关注点
- 应核对各试验协变量缺失结构、跨试验信息融合和目标人群的识别条件，以及规则最优性的定义、评估方式与缺失变量处理。
- 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；摘要未给出融合与缺失机制假设、统计保证或应用效果，不能据此认定规则具有临床有效性。
原文摘要
A fundamental principle of clinical medicine is that a treatment should only be administered to those patients who would benefit from it. Treatment strategies that assign treatment to patients as a function of their individual characteristics are known as dynamic treatment rules. The dynamic treatment rule that optimizes the outcome in the population is called the optimal dynamic treatment rule. Randomized clinical trials are considered the gold standard for estimating the marginal causal effect of a treatment on an outcome; they are often not powered to detect heterogeneous treatment effects, and thus, may rarely inform more personalized treatment decisions. The availability of multiple trials studying a common set of treatments presents an opportunity for combining data, often called data-fusion, to better estimate dynamic treatment rules. However, there may be a mismatch in the set of patient covariates measured across trials. We address this problem here; we propose a nonparametric estimator for the optimal dynamic treatment rule that leverages information across the set of randomized trials. We apply the estimator to fused randomized trials of medications for the treatment of opioid use disorder to estimate a treatment rule that would match patient subgroups with the medication that would minimize risk of return to regular opioid use.