Distributed lag nonlinear models (DLNMs) in Stata

This methodology rests on the definition of a crossbasis, a bidimensional functional space combining two sets of basis functions that specify the relationships in the dimensions of predictor and lags, respectively. DLNMs have been widely used in environmental epidemiology to investigate the short-term associations between environmental exposures, such as weather variables or air pollution, and health outcomes, such as mortality counts or disease-specific hospital admissions. We implemented the DLNMs framework in Stata through the crossbasis command to generate the basis variables that can be fit in a broad range of regression models. In addition, the postestimation commands crossbgraph and crossbslicesallow interpreting the results, emphasizing graphical representation, after the regression model fit. We present an overview of the capabilities of these new community-contributed commands and describe the practical steps to fit and interpret DLNMs with an example of real data to represent the relationship between temperature and mortality in London during the period 2002–2006.