Bayesian Treatment Effect Weight Estimation for Censored Data

This function estimates Bayesian importance sampling weights for treatment models and censoring models across multiple time points via JAGS

Usage

bayesweight_cen(
  trtmodel.list,
  cenmodel.list,
  data,
  n.chains = 2,
  n.iter = 25000,
  n.burnin = 15000,
  n.thin = 5,
  seed = NULL,
  parallel = TRUE
)

Arguments

trtmodel.list: A list of formulas corresponding to each time point with the time-specific treatment variable on the left-hand side and pre-treatment covariates to be balanced on the right-hand side. The formulas must be in temporal order, and must contain all covariates to be balanced at that time point. Interactions and functions of covariates are allowed.
cenmodel.list: A list of formulas for the censored data at each time point, with censoring indicators on the left-hand side and covariates on the right-hand side. The formulas must be in temporal order, and must contain all covariates to be balanced at that time point.
data: A data set in the form of a data frame containing the variables in "trtmodel.list" and "cenmodel.list". This must be a wide data set with exactly one row per unit.
n.chains: Integer specifying the number of MCMC chains to run. Set to 1 for non-parallel computation. For parallel computation, it is required to use at least 2 chains. The default is 2.
n.iter: Integer specifying the total number of iterations for each chain (including burn-in). The default is 25000.
n.burnin: Integer specifying the number of burn-in iterations for each chain. The default is 15000.
n.thin: Integer specifying the thinning rate for the MCMC sampler. The default is 5.
seed: Starting seed for the JAGS model. The default is NULL.
parallel: Logical scalar indicating whether to run the MCMC chains in parallel. The default is TRUE.

Value

A list of the calculated weights and the JAGS model where "weights" is a vector of posterior mean weights, computed by taking the average of the weights across all MCMC iterations and `model_string` is a character of the JAGS model based on the input of "trtmodel.list".

Examples

simdat_cen <- read.csv(system.file("extdata",
                       "sim_causal.csv",
                       package = "bayesmsm"))
weights_cen <- bayesweight_cen(
                trtmodel.list = list(
                A1 ~ L11 + L21,
                A2 ~ L11 + L21 + L12 + L22 + A1,
                A3 ~ L11 + L21 + L12 + L22 + A1 + L13 + L23 + A2),
                cenmodel.list = list(
                C1 ~ L11 + L21,
                C2 ~ L11 + L21 + A1,
                C3 ~ L11 + L21 + A1 + L12 + L22 + A2),
                data = simdat_cen,
                n.chains = 1,
                n.iter = 20,
                n.burnin = 10,
                n.thin = 1,
                seed = 890123,
                parallel = FALSE)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 4836
#>    Unobserved stochastic nodes: 44
#>    Total graph size: 16501
#> 
#> Initializing model
#> 
#> Warning: Some parameters have not converged with Geweke index > 1.96. More iterations may be needed.
summary(weights_cen)
#>              Length Class  Mode     
#> weights      500    -none- numeric  
#> model_string   1    -none- character