CIMTx (version 0.3.0)

iptw_multiTrt: Inverse probability of treatment weighting (IPTW)

Description

This function implements the IPTW method. Please use our main function causal_multi_treat.R.

Usage

iptw_multiTrt(
  y,
  trt,
  psdat,
  estimand = "ATE",
  method,
  trim_alpha = parent.frame()$trim_alpha,
  SL.library = parent.frame()$SL.library,
  reference = parent.frame()$reference_trt
)

Arguments

y

numeric vector for the binary outcome

trt

numeric vector for the treatment indicator

psdat

data frame containing the treatment indicator and covariates

estimand

causal estimands, "ATT" or "ATE"

method

methods for causal inference with multiple treatments, inherited from causal_multi_treat.R

trim_alpha

alpha values for IPTW weight trimming, inherited from causal_multi_treat.R

SL.library

methods specified with SL.library in Superlearner package, inherited from causal_multi_treat.R

reference

Reference group for ATT

Value

list with 2 elements for ATT effect. It contains

ATT12:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

ATT13:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

list with 3 elements for ATE effect. It contains

ATE12:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

ATE13:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

ATE23:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

Examples

# NOT RUN {
library(CIMTx)
set.seed(1)
idata = data_gen(n = 500, ratio =1,scenario = 1)
trt_ind <- as.numeric(idata$trtdat$trt_ind)
all_vars <- idata$trtdat[, -1] #exclude treatment indicator
y <- idata$Yobs
causal_multi_treat(y = y,trt = trt_ind,
method = "IPTW-Logistics", estimand = "ATT", reference_trt = 2)
# }