论文标题
对最佳个性化治疗规则的隐私保护估计：一种案例研究，以最大程度地提高与抑郁症相关的结果
Privacy-preserving estimation of an optimal individualized treatment rule : A case study in maximizing time to severe depression-related outcomes
论文作者
论文摘要
估计个性化的治疗规则 - 尤其是在右审核结果的背景下 - 很具有挑战性，因为治疗效果的异质性通常很小，因此很难检测到。尽管这促使使用非常大的数据集（例如来自多个卫生系统或中心的数据集），但数据隐私可能会引起参与数据中心的关注，而不愿意共享个人级别数据。在有关抑郁症治疗的案例研究中，我们证明了分布式回归用于与动态加权生存建模（DWSURV）结合使用的隐私保护的应用，以估算最佳的个性化治疗规则，同时掩盖了个体级别的数据。在模拟中，我们证明了这种方法的灵活性，以解决可能影响混杂的局部治疗实践，并表明DWSURV即使通过（加权）分布式回归方法进行时，DWSURV仍保留其双重鲁棒性。这项工作是由英国的临床实践研究数据链接进行的，并以对单极抑郁的治疗进行分析。
Estimating individualized treatment rules - particularly in the context of right-censored outcomes - is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.
