Title: Distributed lag non-linear models with Laplacian-P-splines for analysis of spatially structured time series Show Chinese title

Title: 带有拉普拉斯-P-样条的分布滞后非线性模型用于空间结构时间序列的分析

Abstract: Distributed lag non-linear models (DLNM) have gained popularity for modeling nonlinear lagged relationships between exposures and outcomes. When applied to spatially referenced data, these models must account for spatial dependence, a challenge that has yet to be thoroughly explored within the penalized DLNM framework. This gap is mainly due to the complex model structure and high computational demands, particularly when dealing with large spatio-temporal datasets. To address this, we propose a novel Bayesian DLNM-Laplacian-P-splines (DLNM-LPS) approach that incorporates spatial dependence using conditional autoregressive (CAR) priors, a method commonly applied in disease mapping. Our approach offers a flexible framework for capturing nonlinear associations while accounting for spatial dependence. It uses the Laplace approximation to approximate the conditional posterior distribution of the regression parameters, eliminating the need for Markov chain Monte Carlo (MCMC) sampling, often used in Bayesian inference, thus improving computational efficiency. The methodology is evaluated through simulation studies and applied to analyze the relationship between temperature and mortality in London. Show Chinese abstract

Abstract: 分布式滞后非线性模型（DLNM）因其能够建模暴露与结局之间非线性的滞后关系而广受欢迎。当这些模型应用于空间标记数据时，必须考虑空间依赖性，这在惩罚性DLNM框架内尚未得到充分探索。这一差距主要是由于模型结构的复杂性和高计算需求，特别是在处理大规模时空数据集时。为了解决这个问题，我们提出了一种新的贝叶斯DLNM-Laplace-P样条（DLNM-LPS）方法，该方法利用条件自回归（CAR）先验来纳入空间依赖性，这种方法在疾病制图中常被使用。我们的方法提供了一个灵活的框架，用于捕捉非线性关联的同时考虑空间依赖性。它使用拉普拉斯近似来逼近回归参数的条件后验分布，从而消除了对马尔可夫链蒙特卡洛（MCMC）抽样的需求，这通常是贝叶斯推断中使用的，从而提高了计算效率。该方法通过模拟研究进行了评估，并应用于分析伦敦的温度与死亡率之间的关系。