adj_coexposure: Adjusting for expected changes in co-exposure (TDLMM)

In dlmtree: Bayesian Treed Distributed Lag Models

Adjusting for expected changes in co-exposure (TDLMM)

Description

Estimates the marginal effects of an exposure while accounting for expected changes in co-occurring exposures at the same time point. Values of co-occurring exposures are modeled nonlinearly using a spline model with predictions made at the lower an upper values for the exposure of interest.

Usage

adj_coexposure(
  exposure.data,
  object,
  contrast_perc = c(0.25, 0.75),
  contrast_exp = list(),
  conf.level = 0.95,
  keep.mcmc = FALSE,
  verbose = TRUE
)

Arguments

exposure.data	Named list of exposure matrices used as input to TDLMM.
object	Model output for TDLMM from dlmtree() function.
contrast_perc	2-length vector of percentiles or named list corresponding to lower and upper exposure percentiles of interest. Names must equal list names in 'exposure.data'.
contrast_exp	Named list consisting lower and upper exposure values. This takes precedence over contrast_perc if both inputs are used.
conf.level	Confidence level used for estimating credible intervals. Default is 0.95.
keep.mcmc	If TRUE, return posterior samples.
verbose	TRUE (default) or FALSE: print output

Details

adj_coexposure

Value

A list with the following components (or posterior samples if keep.mcmc = TRUE):

Name	vector of exposure names
Time	integer vector of lags
Effect	posterior mean of marginal effects
SE	standard error of the estimate
Lower	lower bound of credible interval of the marginal effect estimate
Upper	upper bound of credible interval of the marginal effect estimate
cEffect	cumulative marginal effects
cLower	lower bound of credible interval of the cumulative marginal effect
cUpper	upper bound of credible interval of the cumulative marginal effect
CW	boolean vector indicating critical window

dlmtree documentation built on June 8, 2025, 12:39 p.m.