Allotey Prince Addo, Harel Ofer
Department of Statistics, University of Connecticut, 215 Glenbrook Rd Unit 4120, Storrs, 06269-4120, CT, USA.
Spat Stat. 2023 Apr;54:100730. doi: 10.1016/j.spasta.2023.100730. Epub 2023 Feb 20.
Survival models which incorporate frailties are common in time-to-event data collected over distinct spatial regions. While incomplete data are unavoidable and a common complication in statistical analysis of spatial survival research, most researchers still ignore the missing data problem. In this paper, we propose a geostatistical modeling approach for incomplete spatially correlated survival data. We achieve this by exploring missingness in outcome, covariates, and spatial locations. In the process, we analyze incomplete spatially-referenced survival data using a Weibull model for the baseline hazard function and correlated log-Gaussian frailties to model spatial correlation. We illustrate the proposed method with simulated data and an application to geo-referenced COVID-19 data from Ghana. There are several disagreements between parameter estimates and credible intervals widths obtained using our proposed approach and complete case analysis. Based on these findings, we argue that our approach provides more reliable parameter estimates and has higher predictive accuracy.
在不同空间区域收集的事件发生时间数据中，纳入脆弱性的生存模型很常见。虽然不完整数据在空间生存研究的统计分析中不可避免且是常见的复杂问题，但大多数研究人员仍然忽略了缺失数据问题。在本文中，我们针对不完整的空间相关生存数据提出了一种地理统计建模方法。我们通过探索结局、协变量和空间位置中的缺失情况来实现这一点。在此过程中，我们使用威布尔模型作为基线危险函数，并使用相关对数高斯脆弱性来对空间相关性进行建模，从而分析不完整的空间参考生存数据。我们用模拟数据以及对来自加纳的地理参考COVID-19数据的应用来说明所提出的方法。使用我们提出的方法获得的参数估计和可信区间宽度与完全病例分析之间存在一些差异。基于这些发现，我们认为我们的方法提供了更可靠的参数估计并且具有更高的预测准确性。
