SBdecomp (version 1.2)

SBdecomp-package: SBdecomp

Description

SBdecomp

Details

The DESCRIPTION file: SBdecomp SBdecomp This packge provides a function that decomposes the estimated selection bias to quantify what proportion of the estimated selection bias is explained by each observed confounder used in the propensity score model; the function is sbdecomp. The function offers two approaches - confounder inclusion or removal, and offers two estimation approaches - parametric or nonparametric. These methods allow one to identify the most important confounder when estimating a propensity score weighted treatment effect in the presence of selection bias.

References

Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476.

Examples

# NOT RUN {
data(petsdata)
# }
# NOT RUN {
sbdecomp(outcome = petsdata$genhealth, treatment = petsdata$gotdog, confounders = 
as.data.frame(petsdata[,c(2:13)]), type = "inclusion", estimation = "parametric")
# }
# NOT RUN {
# }
# NOT RUN {
sbdecomp(outcome = petsdata$genhealth, treatment = petsdata$gotdog, confounders = 
as.data.frame(petsdata[,c(2:13)]), type = "inclusion", estimation = "parametric", 
Bonly =FALSE, balance = TRUE) 
# }
# NOT RUN {
# }
# NOT RUN {
sbdecomp(outcome = "genhealth", treatment = "gotdog", confounders = c("age",
"ismale", "race_coll","hhsize","ownhome", "married",  "ontanf", "hhincome",
"fulltime","spouse_fulltime" ,"liveinhouse", "ruralurban"), data = petsdata, 
type = "inclusion", estimation = "parametric", Bonly =FALSE, balance = TRUE)
# }
# NOT RUN {
# }