Distributed Lag Non-Linear Models (DLNMs) in Stata

The distributed lag non-linear models (DLNMs) represent a modelling framework to flexibly describe associations showing potentially non-linear and delayed effects in time-series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space combining two sets of basis functions, which 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 fitted in a broad range of regression models. In addition, the post estimation commands crossbgraph and crossbslices allow interpreting the results, emphasizing graphical representation, after the regression model fit. We present an overview of the capabilities of these new user-developed 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.