Qiu Xin, Zeng Donglin, Wang Yuanjia
Department of Biostatistics, Columbia University, New York, NY, U.S.A.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.
Biometrics. 2018 Jun;74(2):517-528. doi: 10.1111/biom.12773. Epub 2017 Sep 28.
In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely to be far from optimal, effective methods to construct such rules must guarantee performance, in terms of yielding the best clinical outcome (highest reward) among the class of simple rules under consideration. Furthermore, it is important to evaluate the benefit of the derived rules on the whole sample and in pre-specified subgroups (e.g., vulnerable patients). To achieve both goals, we propose a robust machine learning method to estimate a linear treatment rule that is guaranteed to achieve optimal reward among the class of all linear rules. We then develop a diagnostic measure and inference procedure to evaluate the benefit of the obtained rule and compare it with the rules estimated by other methods. We provide theoretical justification for the proposed method and its inference procedure, and we demonstrate via simulations its superior performance when compared to existing methods. Lastly, we apply the method to the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial on major depressive disorder and show that the estimated optimal linear rule provides a large benefit for mildly depressed and severely depressed patients but manifests a lack-of-fit for moderately depressed patients.
在临床实践中，一条信息丰富且实用的治疗规则应该简单明了。然而，由于简单规则可能远非最优，构建此类规则的有效方法必须在考虑的简单规则类别中，就产生最佳临床结果（最高回报）而言保证其性能。此外，评估推导规则在整个样本以及预先指定的亚组（例如，脆弱患者）中的益处很重要。为实现这两个目标，我们提出一种稳健的机器学习方法来估计线性治疗规则，该规则保证在所有线性规则类别中实现最优回报。然后，我们开发一种诊断度量和推断程序，以评估所获得规则的益处，并将其与其他方法估计的规则进行比较。我们为所提出的方法及其推断程序提供理论依据，并通过模拟证明与现有方法相比其优越的性能。最后，我们将该方法应用于针对重度抑郁症的缓解抑郁症序贯治疗替代方案（STAR*D）试验，结果表明，估计的最优线性规则对轻度抑郁症患者和重度抑郁症患者有很大益处，但对中度抑郁症患者表现出拟合不足。
