We consider a continuous-time proportional hazards model for the analysis of ecological monitoring data where subjects are monitored at discrete times and fixed sites across space. Since the exact time of event occurrence is not directly observed, we rely on dichotomous event indicators observed at monitoring times to make inference about the model parameters. We use autoregression on the response at neighboring sites from a previous time point to take into account spatial dependence. The interesting fact is utilized that the probability of observing an event at a monitoring time when the underlying hazards is proportional falls under the class of generalized linear models with binary responses and complementary log-log link functions. Thus, a maximum likelihood approach can be taken for inference and the computation can be carried out using standard statistical software packages. This approach has significant computational advantages over some of the existing methods that rely on Monte Carlo simulations. Simulation experiments are conducted and demonstrate that our method has sound finite-sample properties. A real dataset from an ecological study that monitored bark beetle colonization of red pines in Wisconsin is analyzed using the proposed models and inference. Supplementary materials that contain technical details are available online.

中文翻译：

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生态监测数据的连续时间比例危害回归。

我们考虑使用连续时间比例风险模型来分析生态监测数据，其中在离散时间和跨空间的固定地点监测受试者。由于不能直接观察到事件发生的确切时间，我们依靠在监测时间观察到的二分事件指标来推断模型参数。我们对来自前一个时间点的相邻站点的响应使用自回归来考虑空间依赖性。一个有趣的事实是，当潜在危险成比例时，在监视时间观察事件的概率属于具有二元响应和互补对数-对数链接函数的广义线性模型的类别。因此，可以采用最大似然方法进行推理，并且可以使用标准统计软件包进行计算。与依赖蒙特卡罗模拟的一些现有方法相比，这种方法具有显着的计算优势。进行了模拟实验并证明我们的方法具有良好的有限样本特性。使用建议的模型和推论分析了来自监测威斯康星州红松树皮甲虫定植的生态研究的真实数据集。包含技术细节的补充材料可在线获取。进行了模拟实验并证明我们的方法具有良好的有限样本特性。使用建议的模型和推理分析了来自监测威斯康星州红松树皮甲虫定植的生态研究的真实数据集。包含技术细节的补充材料可在线获取。进行了模拟实验并证明我们的方法具有良好的有限样本特性。使用建议的模型和推理分析了来自监测威斯康星州红松树皮甲虫定植的生态研究的真实数据集。包含技术细节的补充材料可在线获取。