Selecting Optimal Subgroups for Treatment Using Many Covariates
Selecting Optimal Subgroups for Treatment Using Many Covariates
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DOI：
10.1097/ede.0000000000000991
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发表时间：
2019-05-01
期刊：
影响因子：
5.4
通讯作者：
Kessler, Ronald C.
中科院分区：
文献类型：
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作者：
VanderWeele, Tyler J.;Luedtke, Alex R.;Kessler, Ronald C.
我们考虑的问题，选择最佳的亚组治疗时，协变量的数据可从随机试验或观察性研究。我们区分了四种不同的环境，包括：（1）资源受限时的治疗选择;（2）资源不受限时的治疗选择;（3）存在副作用和成本的治疗选择;（4）最大化效应异质性的治疗选择。我们发现，在每一种情况下，最佳的治疗选择规则涉及治疗那些预测的平均差异的结果比较与不治疗，条件协变量，超过一定的阈值。阈值在这四种情况下各不相同，但最佳治疗选择规则的形式则不同。结果表明，个性化医疗远离传统的亚组分析。提出了新的随机试验设计，以便在医疗实践中实施和利用最佳治疗选择规则。
We consider the problem of selecting the optimal subgroup to treat when data on covariates are available from a randomized trial or observational study. We distinguish between four different settings including: (1) treatment selection when resources are constrained; (2) treatment selection when resources are not constrained; (3) treatment selection in the presence of side effects and costs; and (4) treatment selection to maximize effect heterogeneity. We show that, in each of these cases, the optimal treatment selection rule involves treating those for whom the predicted mean difference in outcomes comparing those with versus without treatment, conditional on covariates, exceeds a certain threshold. The threshold varies across these four scenarios, but the form of the optimal treatment selection rule does not. The results suggest a move away from the traditional subgroup analysis for personalized medicine. New randomized trial designs are proposed so as to implement and make use of optimal treatment selection rules in healthcare practice.