在大型多区域队列研究中，生存数据通常在较小的地理层面（例如县）收集，并在较大的层面上汇总，从而产生与位置相关的相关模式。传统研究通常按区域全局或本地分析此类数据，常常忽略数据中固有的空间信息，这可能会在效果估计中引入偏差，并可能降低统计功效。我们提出了一种用于空间生存数据的地理加权加速故障时间模型来研究空间异质性。我们建立基于准似然信息标准的加权方案和带宽选择。所提出的估计量的理论特性得到了彻底的检验。为了证明模型在各种情况下的有效性，我们使用不同的样本量以及是否遵守比例风险假设进行了模拟研究。此外，我们应用所提出的方法来分析来自新泽西州癌症登记处的监测、流行病学和最终结果的卵巢癌生存数据。我们的模拟结果表明，与现有模型相比，所提出的模型在四项测量方面表现出优越的性能当违反比例风险假设时，方法，包括地理加权 Cox 模型。此外，在每个位置的样本量为 20-25 的场景中，模拟数据无法拟合局部模型，而我们提出的模型仍然表现出令人满意的性能。在实证研究中，我们确定了所有三个协变量影响的明显空间变化。与全局和局部模型相比，我们提出的模型提供了一种探索生存数据空间异质性的新方法，当比例不满足危险假设。它解决了由于样本有限，某些县的生存数据无法拟合模型的问题，特别是在罕见疾病的背景下。© 2024。作者。

In large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power.We propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey.Our simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates.Our proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.© 2024. The Author(s).