bayesmsm (version 1.0.0)

bayesweight_cen: Bayesian Treatment Effect Weight Estimation for Censored Data

Description

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
)

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".

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.

Examples

amodel <- list(
 c("(Intercept)" = -0.3, "L1_1" = 0.4, "L2_1" = -0.2),
 c("(Intercept)" = -0.1, "L1_2" = 0.3, "L2_2" = -0.1, "A_prev" = 0.5))
ymodel <- c(
"(Intercept)" = -0.8,
"A1"          = 0.2,
"A2"          = 0.4,
"L1_2"        = 0.3,
"L2_2"        = -0.3)
cmodel <- list(
 c("(Intercept)" = -1.5, "L1_1" = 0.2, "L2_1" = -0.2, "A" = 0.2),
 c("(Intercept)" = -1.5, "L1_2" = 0.1, "L2_2" = -0.1, "A" = 0.3))
testdata <- simData(
 n                = 50,
 n_visits         = 2,
 covariate_counts = c(2, 2),
 amodel           = amodel,
 ymodel           = ymodel,
 y_type           = "binary",
 right_censor     = TRUE,
 cmodel           = cmodel,
 seed             = 123
)
weights_cen <- bayesweight_cen(
                trtmodel.list = list(
                       A1 ~ L1_1 + L2_1,
                       A2 ~ L2_2 + L2_2 + A1),
                cenmodel.list = list(
                C1 ~ L1_1 + L2_1 + A1,
                C2 ~ L1_2 + L2_2 + A2),
                data = testdata,
                n.chains = 1,
                n.iter = 20,
                n.burnin = 10,
                n.thin = 1,
                seed = 890123,
                parallel = FALSE)
summary(weights_cen)