目的通过R软件实现分布滞后非线性模型(distributed lag non-linear model,DLNM)与多元meta分析的两阶段分析。方法通过1个时间序列数据实例研究介绍利用R软件实现DLNM与多元meta分析2阶段分析的具体步骤。结果DLNM与多元meta分析的两...目的通过R软件实现分布滞后非线性模型(distributed lag non-linear model,DLNM)与多元meta分析的两阶段分析。方法通过1个时间序列数据实例研究介绍利用R软件实现DLNM与多元meta分析2阶段分析的具体步骤。结果DLNM与多元meta分析的两阶段分析可以在R软件中实现。结论R软件可灵活方便地构建DLNM以及实现多元meta分析,在环境流行病学中可广泛应用。展开更多
Introduction Assessment of environmental health effects arising from exposure to multiple substances is often very challenging.This is particularly true when humans are exposed to a mixture that contains both benefici...Introduction Assessment of environmental health effects arising from exposure to multiple substances is often very challenging.This is particularly true when humans are exposed to a mixture that contains both beneficial and harmful substances.A good example relates to the risk and benefits of fish consumption.展开更多
文摘目的通过R软件实现分布滞后非线性模型(distributed lag non-linear model,DLNM)与多元meta分析的两阶段分析。方法通过1个时间序列数据实例研究介绍利用R软件实现DLNM与多元meta分析2阶段分析的具体步骤。结果DLNM与多元meta分析的两阶段分析可以在R软件中实现。结论R软件可灵活方便地构建DLNM以及实现多元meta分析,在环境流行病学中可广泛应用。
基金Alberta Health,Alberta Innovates,the Canada Research Chairs Program,the Canadian Institutes of Health Research,and the Natural Sciences and Engineering Research Council of Canada
文摘Introduction Assessment of environmental health effects arising from exposure to multiple substances is often very challenging.This is particularly true when humans are exposed to a mixture that contains both beneficial and harmful substances.A good example relates to the risk and benefits of fish consumption.
