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.

估计个体化治疗规则——特别是在右删失结果的背景下——具有挑战性，因为感兴趣的治疗效果异质性通常很小，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，但数据隐私可能会与不愿共享个人级别数据的参与数据中心有关。在这个关于抑郁症治疗的案例研究中，我们展示了分布式回归在隐私保护中的应用，并结合动态加权生存模型 (DWSurv) 来估计最佳个体化治疗规则，同时模糊个人层面的数据。在模拟中，我们展示了这种方法在解决可能影响混杂的局部治疗实践方面的灵活性，并表明即使通过（加权）分布式回归方法执行 DWSurv 仍保持其双重稳健性。这项工作的动机和说明是使用英国临床实践研究数据链对单相抑郁症的治疗进行分析。