Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada.
Lifetime Data Anal. 2022 Jul;28(3):512-542. doi: 10.1007/s10985-022-09554-8. Epub 2022 May 2.
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情况下——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。虽然这促使人们使用来自多个医疗系统或中心的大型数据集，但数据隐私可能是一个问题，因为参与的数据中心不愿意共享个人层面的数据。在这个关于抑郁症治疗的案例研究中，我们展示了一种分布式回归在隐私保护方面的应用，它与动态加权生存模型（DWSurv）结合使用，以在隐藏个人层面数据的情况下估计最优个体化治疗规则。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，并表明即使通过（加权）分布式回归方法进行 DWSurv 也保持了其双重稳健性。这项工作的动机是并通过使用英国临床实践研究数据链接对单相抑郁症的治疗进行分析来说明。
