论文标题
部分可观测时空混沌系统的无模型预测
Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable
论文作者
论文摘要
在生存环境中，具有估计最佳治疗方案的大量文献存在，其中根据个人特征分配治疗以最大程度地提高生存概率。这些方法假设一组协变量足以解除治疗结果关系。但是，在观察性研究或不遵守的随机试验中，该假设可能受到限制。因此，我们提出了一种新的方法，用于估计某些混杂因素不可观察并提供二元仪器变量时估算最佳治疗方案。具体而言，通过二元仪器变量，我们通过最大化Kaplan-Meier-Meier样估计器的生存功能提出了半参数估计器，以实现最佳治疗方案。此外，为了提高对模型错误指定的抗性，我们构建了新型的双重稳健估计器。由于生存函数的估计量被锯齿状，因此我们结合了内核平滑方法以提高性能。在适当的规律性条件下，严格确定渐近性能。此外，通过模拟研究评估有限样本性能。最后，我们使用国家癌症研究所前列腺，肺，结直肠和卵巢癌筛查试验的数据说明了我们的方法。
In survival contexts, substantial literature exists on estimating optimal treatment regimes, where treatments are assigned based on personal characteristics to maximize the survival probability. These methods assume that a set of covariates is sufficient to deconfound the treatment-outcome relationship. However, this assumption can be limited in observational studies or randomized trials in which non-adherence occurs. Therefore, we propose a novel approach to estimating optimal treatment regimes when certain confounders are unobservable and a binary instrumental variable is available. Specifically, via a binary instrumental variable, we propose a semiparametric estimator for optimal treatment regimes by maximizing a Kaplan-Meier-like estimator of the survival function. Furthermore, to increase resistance to model misspecification, we construct novel doubly robust estimators. Since the estimators of the survival function are jagged, we incorporate kernel smoothing methods to improve performance. Under appropriate regularity conditions, the asymptotic properties are rigorously established. Moreover, the finite sample performance is evaluated through simulation studies. Finally, we illustrate our method using data from the National Cancer Institute's prostate, lung, colorectal, and ovarian cancer screening trial.
