最佳个体化治疗规则的隐私保护估计：最大化严重抑郁症相关结局时间的病例研究
Privacy-preserving estimation of an optimal individualized treatment rule : A case study in maximizing time to severe depression-related outcomes
摘要：
估计个体化治疗规则-特别是在右删失结局的背景下-具有挑战性，因为关注的处理效应异质性通常很小，因此难以检测。虽然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不愿共享个人数据的担忧。在这个抑郁症治疗的案例研究中，我们展示了一个应用程序的隐私保护与动态加权生存模型（DWSurv）相结合，以估计一个最佳的个性化治疗规则，同时模糊个人层面的数据。在模拟中，我们证明了这种方法的灵活性，以解决当地的治疗做法，可能会影响混淆，并显示DWSurv保持其双重鲁棒性，即使通过（加权）分布式回归方法进行。这项工作的动机是，并说明了，使用英国的临床实践研究数据链治疗单相抑郁症的分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
