Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China.
BMC Med Res Methodol. 2023 Oct 13;23(1):233. doi: 10.1186/s12874-023-02055-8.
When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites.
The proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients.
The simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method.
The proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis.
当数据分布在多个站点时，在站点之间共享个体水平的信息可能很困难。在这些多站点研究中，可以使用逆概率加权 Cox 回归来估计总体危险比，在每个站点内拟合倾向评分模型或在所有站点内拟合数据。然而，当不同站点中的协变量存在未知异质性时，这两种方法都可能导致潜在的偏差或降低效率。在这项研究中，我们提出了一种基于协变量平衡相关标准估计倾向评分的方法，并估计了克服站点之间数据共享限制的总体危险比。
提出的倾向评分是通过基于协变量平衡相关标准在全局和局部倾向评分之间进行选择来生成的，将在整个人群中拟合的全局倾向评分与在每个站点内拟合的局部倾向评分相结合。我们使用这种建议的倾向评分来估计具有多个站点的分布式生存数据的总体危险比，同时仅需要站点之间的汇总水平信息。我们进行了模拟研究来评估所提出方法的性能。此外，我们将所提出的方法应用于真实世界的数据，以检验放射治疗对乳腺癌患者死亡时间的影响。
模拟研究表明，无论站点数量和每个站点的样本量如何，与全局和局部倾向评分方法相比，所提出的方法在估计总体危险比方面都提高了性能。在同质和异质设置下均观察到相似的结果。此外，所提出的方法产生的结果与汇总个体水平数据分析相同。真实世界数据分析表明，与全局倾向评分方法和局部倾向评分方法相比，所提出的方法更有可能发现放射治疗对死亡率的显著影响。
在所提出的多站点分布式生存数据中，基于协变量平衡的倾向评分优于使用整个人群中的数据估计的全局倾向评分或在每个站点内估计的局部倾向评分，用于估计总体危险比。该方法可以在站点之间不进行个体水平数据传输的情况下进行，并且会产生与相应的汇总个体水平数据分析相同的结果。
