Distributed lag non-linear models (DLNMs) represent a modeling 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 expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical s...
The package dlnm contains functions to specify and interpret distributed lag linear (DLMs) and non-l...
exposure-lag-response associations with distributed lag non-linear models” Antonio Gasparrinia∗† Thi...
In this article, we introduce the R package dLagM for the implementation of distributed lag models a...
: Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe asso...
Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associ...
Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associ...
Environmental stressors often show effects that are delayed in time, requiring the use of statistica...
Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear...
BACKGROUND: The two-stage time series design represents a powerful analytical tool in environmental ...
A tutorial on the use of distributed lag non-linear models in time series analysis, illustrating the...
The distributed lag non-linear model (DLNM) is frequently used in environmental and epidemiological ...
associations with distributed lag non-linear models Antonio Gasparrini*† In biomedical research, a h...
An illustration on methods for reducing estimates of bi-dimensional exposure-lag-response associatio...
In biomedical research, a health effect is frequently associated with protracted exposures of varyin...
R code reproducing the results published in an article on the extension of distributed lag linear an...
The package dlnm contains functions to specify and interpret distributed lag linear (DLMs) and non-l...
exposure-lag-response associations with distributed lag non-linear models” Antonio Gasparrinia∗† Thi...
In this article, we introduce the R package dLagM for the implementation of distributed lag models a...
: Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe asso...
Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associ...
Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associ...
Environmental stressors often show effects that are delayed in time, requiring the use of statistica...
Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear...
BACKGROUND: The two-stage time series design represents a powerful analytical tool in environmental ...
A tutorial on the use of distributed lag non-linear models in time series analysis, illustrating the...
The distributed lag non-linear model (DLNM) is frequently used in environmental and epidemiological ...
associations with distributed lag non-linear models Antonio Gasparrini*† In biomedical research, a h...
An illustration on methods for reducing estimates of bi-dimensional exposure-lag-response associatio...
In biomedical research, a health effect is frequently associated with protracted exposures of varyin...
R code reproducing the results published in an article on the extension of distributed lag linear an...
The package dlnm contains functions to specify and interpret distributed lag linear (DLMs) and non-l...
exposure-lag-response associations with distributed lag non-linear models” Antonio Gasparrinia∗† Thi...
In this article, we introduce the R package dLagM for the implementation of distributed lag models a...