SBdecomp-package {SBdecomp} | R Documentation |
Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. 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 <doi: 10.1002/sim.8549>.
The DESCRIPTION file:
Package: | SBdecomp |
Type: | Package |
Title: | Estimation of the Proportion of SB Explained by Confounders |
Version: | 1.2 |
Date: | 2021-11-14 |
Author: | Layla Parast |
Maintainer: | Layla Parast <parast@austin.utexas.edu> |
Description: | Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. 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 <doi: 10.1002/sim.8549>. |
License: | GPL |
Imports: | stats, twang, graphics, survey |
Index of help topics:
SBdecomp-package Estimation of the Proportion of SB Explained by Confounders bar.sbdecomp Creates a Bar Plot petsdata Dog ownership dataset sbdecomp Selection Bias Decomposition
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.
Layla Parast
Maintainer: Layla Parast <parast@austin.utexas.edu>
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.
data(petsdata)
sbdecomp(outcome = petsdata$genhealth, treatment = petsdata$gotdog, confounders =
as.data.frame(petsdata[,c(2:13)]), type = "inclusion", estimation = "parametric")
sbdecomp(outcome = petsdata$genhealth, treatment = petsdata$gotdog, confounders =
as.data.frame(petsdata[,c(2:13)]), type = "inclusion", estimation = "parametric",
Bonly =FALSE, balance = TRUE)
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)