Constructing dynamic treatment regimes with shared parameters for censored data
Constructing dynamic treatment regimes with shared parameters for censored data
复制标题
使用审查数据的共享参数构建动态治疗方案
作者：
Ying;Ruoqing Zhu;Guanhua Chen;Yingye Zheng
动态治疗方案是根据患者的演变特征在整个疾病进展中适应的顺序决策规则。在许多临床应用中，期望决策规则的格式随时间保持一致。与常规设置中的动态治疗方案的估计不同，在常规设置中，在没有共享参数的情况下形成决策规则，共享决策规则的导出需要估计跨不同决策点索引决策规则的共享参数。当感兴趣的临床结果是受删失影响的生存时间时，这种规则的估计变得更加复杂。为了解决这些挑战，我们提出了两种新的方法：审查共享Q学习和审查共享O学习。这两种方法都将临床偏好纳入定性规则，其中决策规则的参数索引在不同的决策点之间共享并同时估计。我们使用仿真研究来证明所提出的方法的上级性能。该方法进一步应用于心脏病研究，以获得心血管疾病的治疗规则。
Dynamic treatment regimes are sequential decision rules that adapt throughout disease progression according to a patient's evolving characteristics. In many clinical applications, it is desirable that the format of the decision rules remains consistent over time. Unlike the estimation of dynamic treatment regimes in regular settings, where decision rules are formed without shared parameters, the derivation of the shared decision rules requires estimating shared parameters indexing the decision rules across different decision points. Estimation of such rules becomes more complicated when the clinical outcome of interest is a survival time subject to censoring. To address these challenges, we propose two novel methods: censored shared‐Q‐learning and censored shared‐O‐learning. Both methods incorporate clinical preferences into a qualitative rule, where the parameters indexing the decision rules are shared across different decision points and estimated simultaneously. We use simulation studies to demonstrate the superior performance of the proposed methods. The methods are further applied to the Framingham Heart Study to derive treatment rules for cardiovascular disease.
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影响因子：
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