Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: with an Application to Treating Type 2 Diabetes Patients with Insulin Therapies.
Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: with an Application to Treating Type 2 Diabetes Patients with Insulin Therapies.
复制标题
DOI：
10.1080/01621459.2017.1303386
复制
发表时间：
2018
影响因子：
3.7
通讯作者：
Zeng D
中科院分区：
文献类型：
--
作者：
Wang Y;Fu H;Zeng D
由于患者治疗反应的异质性，个性化医疗决策往往是复杂的。药物治疗对不同的患者群体可能表现出不同的疗效和安全性。使患者临床获益最大化的“最佳”治疗也可能由于不良事件的高风险而引起对安全性的担忧。因此，为了指导个性化的临床决策和提供最佳的定制治疗，应在控制潜在风险的背景下考虑最大化临床效益。在这项工作中，我们提出了两种方法来确定个性化的最佳治疗策略，在平均风险的约束下最大化临床效益。我们推导了风险约束下的理论最优处理规则，并类比Neyman-Pearson引理来证明该定理。我们提出的算法可以很容易地实现任何现成的二次规划包。我们进行了广泛的模拟研究，以显示在最大化临床效益的同时令人满意的风险控制。最后，我们将我们的方法应用于一项2型糖尿病患者的随机试验，以指导基于个体患者特征的一线胰岛素治疗的最佳利用，同时控制低血糖事件的发生率。我们确定基线糖化血红蛋白水平、体重指数和空腹血糖是18个生物标志物中区分治疗分配的三个关键因素，并在训练和测试数据集中证明了对低血糖风险的成功控制。
Individualized medical decision making is often complex due to patient treatment response heterogeneity. Pharmacotherapy may exhibit distinct efficacy and safety profiles for different patient populations. An “optimal” treatment that maximizes clinical benefit for a patient may also lead to concern of safety due to a high risk of adverse events. Thus, to guide individualized clinical decision making and deliver optimal tailored treatments, maximizing clinical benefit should be considered in the context of controlling for potential risk. In this work, we propose two approaches to identify personalized optimal treatment strategy that maximizes clinical benefit under a constraint on the average risk. We derive the theoretical optimal treatment rule under the risk constraint and draw an analogy to the Neyman-Pearson lemma to prove the theorem. We present algorithms that can be easily implemented by any off-the-shelf quadratic programming package. We conduct extensive simulation studies to show satisfactory risk control when maximizing the clinical benefit. Lastly, we apply our method to a randomized trial of type 2 diabetes patients to guide optimal utilization of the first line insulin treatments based on individual patient characteristics while controlling for the rate of hypoglycemia events. We identify baseline glycated hemoglobin level, body mass index, and fasting blood glucose as three key factors among 18 biomarkers to differentiate treatment assignments, and demonstrate a successful control of the risk of hypoglycemia in both the training and testing data set.
登录
查看更多内容
影响因子：
2
作者：
Foster, Jared C.;Taylor, Jeremy M. G.;Ruberg, Stephen J.
通讯作者：
Ruberg, Stephen J.
影响因子：
4.5
作者：
Qian M;Murphy SA
通讯作者：
Murphy SA
影响因子：
4.5
作者：
Guo, Jeff J.;Pandey, Swapnil;Raisch, Dennis W.
通讯作者：
Raisch, Dennis W.
DOI：
10.1080/01621459.2014.926815
发表时间：
2015-06-01
影响因子：
3.7
作者：
Lee J;Thall PF;Ji Y;Müller P
通讯作者：
Müller P
影响因子：
2.1
作者：
Cai, Tianxi;Tian, Lu;Wei, L. J.
通讯作者：
Wei, L. J.