| SBdecomp-package {SBdecomp} | R Documentation |
Estimation of the Proportion of SB Explained by Confounders
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>.
Details
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.
Author(s)
Layla Parast
Maintainer: Layla Parast <parast@austin.utexas.edu>
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
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)