Liang C Jason, Luo Chongliang, Kranzler Henry R, Bian Jiang, Chen Yong
Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, United States.
Division of Public Health Sciences, Washington University School of Medicine, St Louis, MO 63110, United States.
J Am Med Inform Assoc. 2025 Apr 1;32(4):656-664. doi: 10.1093/jamia/ocae313.
To develop a distributed algorithm to fit multi-center Cox regression models with time-varying coefficients to facilitate privacy-preserving data integration across multiple health systems.
The Cox model with time-varying coefficients relaxes the proportional hazards assumption of the usual Cox model and is particularly useful to model time-to-event outcomes. We proposed a One-shot Distributed Algorithm to fit multi-center Cox regression models with Time varying coefficients (ODACT). This algorithm constructed a surrogate likelihood function to approximate the Cox partial likelihood function, using patient-level data from a lead site and aggregated data from other sites. The performance of ODACT was demonstrated by simulation and a real-world study of opioid use disorder (OUD) using decentralized data from a large clinical research network across 5 sites with 69 163 subjects.
The ODACT method precisely estimated the time-varying effects over time. In the simulation study, ODACT always achieved estimation close to that of the pooled analysis, while the meta-estimator showed considerable amount of bias. In the OUD study, the bias of the estimated hazard ratios by ODACT are smaller than those of the meta-estimator for all 7 risk factors at almost all of the time points from 0 to 2.5 years. The greatest bias of the meta-estimator was for the effects of age ≥65 years, and smoking.
ODACT is a privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data which allows the covariates' effects to be time-varying. ODACT provides estimates close to the pooled estimator and substantially outperforms the meta-analysis estimator.
The proposed ODACT is a privacy-preserving distributed algorithm for fitting Cox models with time-varying coefficients. The limitations of ODACT include that privacy-preserving via aggregate data does rely on relatively large number of data at each individual site, and rigorous quantification of the risk of privacy leaks requires further investigation.
开发一种分布式算法，用于拟合具有时变系数的多中心Cox回归模型，以促进跨多个卫生系统的隐私保护数据整合。
具有时变系数的Cox模型放宽了普通Cox模型的比例风险假设，对于建模事件发生时间结局尤为有用。我们提出了一种用于拟合具有时变系数的多中心Cox回归模型的一次性分布式算法（ODACT）。该算法使用来自牵头站点的患者水平数据和来自其他站点的汇总数据构建了一个替代似然函数，以近似Cox偏似然函数。通过模拟以及对阿片类药物使用障碍（OUD）的一项真实世界研究，利用来自一个大型临床研究网络的5个站点的69163名受试者的分散数据，展示了ODACT的性能。
ODACT方法精确地估计了随时间变化的效应。在模拟研究中，ODACT始终实现接近汇总分析的估计，而元估计器显示出相当大的偏差。在OUD研究中，从0到2.5年的几乎所有时间点，对于所有7个风险因素，ODACT估计的风险比偏差均小于元估计器。元估计器最大的偏差在于年龄≥65岁和吸烟的效应。
ODACT是一种用于分析多中心事件发生时间数据的隐私保护且通信高效的方法，它允许协变量的效应随时间变化。ODACT提供的估计接近汇总估计器，并且显著优于元分析估计器。
所提出的ODACT是一种用于拟合具有时变系数的Cox模型的隐私保护分布式算法。ODACT的局限性包括通过汇总数据进行隐私保护确实依赖于每个单独站点相对大量的数据，并且隐私泄露风险的严格量化需要进一步研究。
