Generalized additive distributed lag models: quantifying mortality displacement.
Generalized additive distributed lag models: quantifying mortality displacement.
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DOI：
10.1093/biostatistics/1.3.279
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发表时间：
2000-09-01
期刊：
影响因子：
--
通讯作者：
Ryan, L M
中科院分区：
文献类型：
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作者：
Zanobetti, A;Wand, M P;Ryan, L M
有许多应用的设置，其中响应随时间重复测量，并且刺激在一个时间的影响分布在几个后续的响应测量中。在激励应用中，刺激是空气污染物，例如空气中的颗粒物，而响应是死亡率。然而，其他几个变量（如每日温度）可能以非线性方式影响响应。为了量化存在协变量数据时刺激的影响，我们联合收割机结合了两种已建立的回归技术：广义加性模型和分布滞后模型。广义加性模型扩展了多元线性回归，允许连续协变量被建模为平滑的，但在其他方面未指定的函数。分布滞后模型的目的是将结果变量与时间依赖预测因子的滞后值以一种简约的方式联系起来。由此产生的，我们称之为广义加性分布滞后模型，被认为是有效地量化所谓的“死亡率位移效应”的环境流行病学，如所示，通过空气污染/死亡率数据从米兰，意大利。
There are a number of applied settings where a response is measured repeatedly over time, and the impact of a stimulus at one time is distributed over several subsequent response measures. In the motivating application the stimulus is an air pollutant such as airborne particulate matter and the response is mortality. However, several other variables (e.g. daily temperature) impact the response in a possibly non-linear fashion. To quantify the effect of the stimulus in the presence of covariate data we combine two established regression techniques: generalized additive models and distributed lag models. Generalized additive models extend multiple linear regression by allowing for continuous covariates to be modeled as smooth, but otherwise unspecified, functions. Distributed lag models aim to relate the outcome variable to lagged values of a time-dependent predictor in a parsimonious fashion. The resultant, which we call generalized additive distributed lag models, are seen to effectively quantify the so-called 'mortality displacement effect' in environmental epidemiology, as illustrated through air pollution/mortality data from Milan, Italy.