This package implements a hierarchical Bayesian logistic regression analysis to identify/estimate critical windows of susceptibility corresponding to exposure from a single time-varying covariate. The method involves temporally smoothed Bayesian variable selection, with correlated Gaussian process smoothness in the risk and variable selection parameters, and is fit using Markov chain Monte Carlo sampling techniques. Please see the "CWVS_Model_Details" and "CWVS_Example" folders for more specific information regarding the statistical model and package use details, respectively.
- Warren JL, Kong W, Luben TJ, and Chang HH (2020). Critical window variable selection: estimating the impact of air pollution on very preterm birth. Biostatistics, 21(4):790-806.
- https://academic.oup.com/biostatistics/article-abstract/21/4/790/5432287?redirectedFrom=fulltext