BACKGROUND Multi-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding pooling of individual-level data in multi-center studies. METHODS By leveraging the Newton-Raphson procedure, we adapted the modified Poisson regression method to estimate multivariable-adjusted risk ratios using only summary-level information in multi-center studies. We developed and tested the proposed method using both simulated and real-world data examples. We compared its results with the results from the corresponding pooled individual-level data analysis. RESULTS Our proposed method produced the same adjusted risk ratio estimates and standard errors as the corresponding pooled individual-level data analysis without pooling individual-level data across data-contributing sites. CONCLUSIONS We developed and validated a distributed modified Poisson regression algorithm for valid and privacy-protecting estimation of adjusted risk ratios and confidence intervals in multi-center studies. This method allows computation of a more interpretable measure of association for binary outcomes, along with valid construction of confidence intervals, without sharing of individual-level data.

中文翻译：

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在多中心研究中使用修正的Poisson回归来保护调整后的风险比率的隐私保护估计。

背景技术多中心研究可以产生可靠且可概括的证据，但是出于隐私考虑和法律限制，通常很难或不可能跨数据提供站点来汇集个人级别的数据。随着二进制结果的出现，已经开发出用于进行逻辑回归分析的保护隐私的分布式算法。但是，与风险比相比，风险比通常可以更清晰地说明风险暴露与结果的关系。有人提出了改进的泊松回归法来直接估算调整后的风险比，并在可获得个人水平数据时以正确的名义覆盖率产生置信区间。当前没有分布式回归算法来估计调整后的风险比率，同时避免在多中心研究中合并单个级别的数据。方法利用牛顿-拉夫森程序，我们修改了Poisson回归方法，以在多中心研究中仅使用汇总信息来估计经多变量调整的风险比。我们使用模拟和实际数据示例开发并测试了所提出的方法。我们将其结果与相应的汇总个人级别数据分析的结果进行了比较。结果我们提出的方法产生了与相应的汇总个人水平数据分析相同的调整后的风险比率估计值和标准误，而没有在数据提供站点之间汇总个人水平数据。结论我们开发并验证了一种分布式修正的Poisson回归算法，用于在多中心研究中对调整后的风险比和置信区间进行有效且具有隐私保护的估计。