论文标题
从多种治疗方法中选择最佳的个性化处理
Selection of the Optimal Personalized Treatment from Multiple Treatments with Right-censored Multivariate Outcome Measures
论文作者
论文摘要
我们提出了一个新型的个性化概念，用于在响应是多元矢量的情况下进行最佳治疗选择，该媒介可能包含右审查的变量，例如生存时间。所提出的方法可以在广泛的模型下使用任何数量的处理和结果变量应用。遵循与协变量和响应相关的工作半参数单索引模型，我们首先定义了由单个协变量构建的患者特异性复合评分。然后，鉴于患者得分，我们使用非参数平滑估计器估算每个反应的条件均值，对应于每种治疗。接下来，采用等级聚合技术来估算基于条件均值给出的治疗绩效指标的排名清单的处理顺序。我们通过将重量加权与相应估计器的逆概率合并到相应的估计器中来处理右审查的数据。一项实证研究说明了在有限样本问题中提出的方法的性能。为了显示所提出的实际数据程序的适用性，我们还使用HIV临床试验数据提出了数据分析，该数据包含右审查的生存事件作为终点之一。
We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector, that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.
