Bayesian Distributed Lag Non-Linear Models (B-DLNM)

A Bayesian framework for estimating distributed lag linear and non-linear models. Model fitting is implemented using Integrated Nested Laplace Approximation (R package 'INLA'), together with prediction and visualization of exposure-lag-response associations. Additional functions allow estimation of optimal exposure values (e.g., minimum mortality temperature) and computation of attributable fractions and numbers. Models with 'crossbasis' or 'onebasis' terms are supported (R package 'dlnm').

bdlnm

The goal of bdlnm is to provide tools for fitting Bayesian distributed lag non-linear models (B-DLNM) using the INLA framework. The package allows users to estimate exposure-lag-response associations, generate posterior predictions, compute attributable risk measures, and estimate optimal exposure values within a fully Bayesian framework.

Key features

The package provides functions to:

• Fit Bayesian distributed lag linear and non-linear models using INLA
• Generate predictions of exposure-lag-response associations
• Estimate attributable fractions and attributable numbers
• Estimate optimal exposure values (e.g., minimum risk exposure)
• Visualize exposure–lag–response associations

More detailed examples and tutorials are available in the package vignettes.

Installation

You can install the development version of bdlnm like so:

devtools::install_github("pasahe/bdlnm")

At least the stable version of INLA 23.4.24 (or newest) must be installed beforehand. You can install the newest stable INLA version by:

install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)

Citation

If you use bdlnm in your research, please cite:

citation("bdlnm")

Acknowledgements

This package extends the distributed lag modelling framework implemented in the dlnm package developed by Gasparrini (2011) to a Bayesian setting.