论文标题
通过签名协定对个性化治疗规则进行荟萃分析
Meta-analysis of individualized treatment rules via sign-coherency
论文作者
论文摘要
根据患者的基线特征量身定制的医疗治疗具有改善患者预后的潜力，同时减少负面影响。学习个性化的治疗规则（ITR）通常需要汇总多个数据集（站点）；但是，当前的ITR方法不考虑现场异质性，这在部署回每个站点时会损害模型的通用性。为了解决这个问题，我们开发了一种对ITR的个人级荟萃分析的方法，该方法共同学习特定于地点的ITR，同时通过科学动机的方向性原理借用有关特征签名的信息。我们还使用针对ITR学习问题量身定制的信息标准开发了模型调整的自适应程序。我们通过数值实验研究了提出的方法，以了解其在不同水平的地点异质性下的性能，并将方法应用于电子健康记录的大型多中心数据库中的ITR。这项工作将估算ITR（A学习，加权学习）估计的几种流行方法扩展到了多个点的设置。
Medical treatments tailored to a patient's baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt model generalizability when deploying back to each site. To address this problem, we develop a method for individual-level meta-analysis of ITRs, which jointly learns site-specific ITRs while borrowing information about feature sign-coherency via a scientifically-motivated directionality principle. We also develop an adaptive procedure for model tuning, using information criteria tailored to the ITR learning problem. We study the proposed methods through numerical experiments to understand their performance under different levels of between-site heterogeneity and apply the methodology to estimate ITRs in a large multi-center database of electronic health records. This work extends several popular methodologies for estimating ITRs (A-learning, weighted learning) to the multiple-sites setting.
