Objective

To characterize the interplay between multiple medical conditions across sites and account for the heterogeneity in patient population characteristics across sites within a distributed research network, we develop a one-shot algorithm that can efficiently utilize summary-level data from various institutions. By applying our proposed algorithm to a large pediatric cohort across four national Children’s hospitals, we replicated a recently published prospective cohort, the RISK study, and quantified the impact of the risk factors associated with the penetrating or stricturing behaviors of pediatric Crohn's disease (PCD).

Methods

In this study, we introduce the ODACoRH algorithm, a one-shot distributed algorithm designed for the competing risks model with heterogeneity. Our approach considers the variability in baseline hazard functions of multiple endpoints of interest across different sites. To accomplish this, we build a surrogate likelihood function by combining patient-level data from the local site with aggregated data from other external sites. We validated our method through extensive simulation studies and replication of the RISK study to investigate the impact of risk factors on the PCD for adolescents and children from four children's hospitals within the PEDSnet, A National Pediatric Learning Health System. To evaluate our ODACoRH algorithm, we compared results from the ODACoRH algorithms with those from meta-analysis as well as those derived from the pooled data.

Results

The ODACoRH algorithm had the smallest relative bias to the gold standard method (−0.2%), outperforming the meta-analysis method (−11.4%). In the PCD association study, the estimated subdistribution hazard ratios obtained through the ODACoRH algorithms are identical on par with the results derived from pooled data, which demonstrates the high reliability of our federated learning algorithms. From a clinical standpoint, the identified risk factors for PCD align well with the RISK study published in the Lancet in 2017 and other published studies, supporting the validity of our findings.

Conclusion

With the ODACoRH algorithm, we demonstrate the capability of effectively integrating data from multiple sites in a decentralized data setting while accounting for between-site heterogeneity. Importantly, our study reveals several crucial clinical risk factors for PCD that merit further investigations.

中文翻译：

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用于解决跨临床站点竞争风险数据异质性的一次性分布式算法

客观的

为了描述跨站点多种医疗状况之间的相互作用，并解释分布式研究网络内跨站点患者群体特征的异质性，我们开发了一种一次性算法，可以有效地利用来自不同机构的汇总级数据。通过将我们提出的算法应用于四家国家儿童医院的大型儿科队列，我们​​复制了最近发表的前瞻性队列 RISK 研究，并量化了与儿科克罗恩病 (PCD) 穿透或狭窄行为相关的风险因素的影响。

方法

在本研究中，我们介绍了 ODACoRH 算法，这是一种针对异构竞争风险模型设计的一次性分布式算法。我们的方法考虑了不同地点的多个感兴趣终点的基线危险函数的变异性。为了实现这一目标，我们通过将本地站点的患者级数据与其他外部站点的聚合数据相结合来构建替代似然函数。我们通过广泛的模拟研究和 RISK 研究的重复验证了我们的方法，以调查危险因素对 PEDSnet（国家儿科学习健康系统）内四家儿童医院的青少年和儿童 PCD 的影响。为了评估我们的 ODACoRH 算法，我们将 ODACoRH 算法的结果与荟萃分析的结果以及汇总数据得出的结果进行了比较。

结果

ODACoRH 算法与金标准方法的相对偏差最小 (-0.2%)，优于荟萃分析方法 (-11.4%)。在 PCD 关联研究中，通过 ODACoRH 算法获得的估计次分布风险比与汇总数据得出的结果相同，这证明了我们的联邦学习算法的高可靠性。从临床角度来看，已确定的 PCD 危险因素与 2017 年《柳叶刀》上发表的 RISK 研究以及其他已发表的研究非常吻合，支持了我们研究结果的有效性。

结论

通过 ODACoRH 算法，我们展示了在分散的数据设置中有效集成来自多个站点的数据的能力，同时考虑了站点之间的异质性。重要的是，我们的研究揭示了 PCD 的几个关键临床危险因素，值得进一步研究。