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This PSweight Package is to perform propensity score weighting analysis for causal inference. Two main modules are included to assist the design and analysis of observational studies. In the design module, the SumStat function is used to generate distributional plots of the estimated propensity scores and balance diagnostics after propensity score weighting. The summary and plot functions are available to tabulate and plot weighted balance statistics for visual comparisons. In the analysis module, the PSweight function, the average potential outcomes for each treatment group is estimated using weighting, and the summary function generates point estimates, standard errors and confidence intervals for the desired causal contrasts of interest. The current version of PSweight package includes the following types of weights: the overlap weights (ATO), the inverse probability of treatment weights (ATE), the average treatment effect among the treated weights (ATT), the matching weights (ATM) and the entropy weights (ATEN), and allows for binary and multiple (categorical) treatments. In addition to the simple weighting estimator, the package also implements the augmented weighting estimator that combines weighting and outcome regression. For binary outcomes, both the additive and ratio estimands (causal relative risk and odds ratio) are considered, and variance is estimated by either the sandwich method or nonparametric bootstrap. To allow for additional flexibility in specifying the propensity score and outcome models, the package can also work with user-supplied propensity score estimates and outcome predictions through ps.estimate and out.estimate, and provide a sandwich standard error that ignores the variability in estimating these nuisances.
You can install the released version of PSweight from CRAN with:
The version 1.1.7 includes module for cluster design. Please check out the help page for PSweight_cl and SumStat_cl.
The version 1.2.0 of PSweight addresses several important bug fixes to enhance the functionality and user experience of our R package. Notable updates include:
1.Enhanced Parameter Support: The “PSmethod” and “OUTmethod” functions have been updated to accurately incorporate parameters specified via “ps.control” and “out.control”.
2.Propensity Score Model Output: Users can now access the fitted propensity score model directly from the “SumStat” function output, facilitating deeper analysis and diagnostics.
3.Improved Handling of Single Covariate Models: The package now includes checks within “summary.SumStat()” and “plot.SumStat()” functions to ensure they operate correctly even when the propensity score model is based on a single covariate.
4.Flexible Treatment Variable Naming: The “SumStat_cl()” function has been modified to replace the hardcoded ‘trt’ variable with a dynamic reference, “data[[zname]]”, allowing users to specify their treatment variable names.
These updates aim to enhance the PSweight package’s functionality, usability, and analytical precision. Users are encouraged to explore the new features and provide feedback for continuous improvement.
This is a basic example on design:
library(PSweight)
example("SumStat")
#>
#> SumStt> data("psdata")
#>
#> SumStt> # the propensity model
#> SumStt> ps.formula<-trt~cov1+cov2+cov3+cov4+cov5+cov6
#>
#> SumStt> # using SumStat to estimate propensity scores
#> SumStt> msstat <- SumStat(ps.formula, trtgrp="2", data=psdata,
#> SumStt+ weight=c("IPW","overlap","treated","entropy","matching"))
#>
#> SumStt> #summary(msstat)
#> SumStt>
#> SumStt> # importing user-supplied propensity scores "e.h"
#> SumStt> # fit <- nnet::multinom(formula=ps.formula, data=psdata, maxit=500, trace=FALSE)
#> SumStt> # e.h <- fit$fitted.values
#> SumStt> # varname <- c("cov1","cov2","cov3","cov4","cov5","cov6")
#> SumStt> # msstat0 <- SumStat(zname="trt", xname=varname, data=psdata, ps.estimate=e.h,
#> SumStt> # trtgrp="2", weight=c("IPW","overlap","treated","entropy","matching"))
#> SumStt> # summary(msstat0)
#> SumStt>
#> SumStt>
#> SumStt>
#> SumStt>This is a basic example on analysis:
example("PSweight")
#>
#> PSwght> data("psdata")
#>
#> PSwght> # the propensity and outcome models
#> PSwght> ps.formula<-trt~cov1+cov2+cov3+cov4+cov5+cov6
#>
#> PSwght> out.formula<-Y~cov1+cov2+cov3+cov4+cov5+cov6
#>
#> PSwght> # without augmentation
#> PSwght> ato1<-PSweight(ps.formula = ps.formula,yname = 'Y',data = psdata,weight = 'overlap')
#>
#> PSwght> summary(ato1)
#>
#> Closed-form inference:
#>
#> Original group value: 1, 2, 3
#>
#> Contrast:
#> 1 2 3
#> Contrast 1 -1 1 0
#> Contrast 2 -1 0 1
#> Contrast 3 0 -1 1
#>
#> Estimate Std.Error lwr upr Pr(>|z|)
#> Contrast 1 -1.24161 0.16734 -1.56960 -0.91362 1.177e-13 ***
#> Contrast 2 1.12482 0.17099 0.78968 1.45996 4.764e-11 ***
#> Contrast 3 2.36643 0.25854 1.85970 2.87315 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> PSwght> # augmented weighting estimator, takes longer time to calculate sandwich variance
#> PSwght> # ato2<-PSweight(ps.formula = ps.formula,yname = 'Y',data = psdata,
#> PSwght> # augmentation = TRUE,out.formula = out.formula,family = 'gaussian',weight = 'overlap')
#> PSwght> # summary(ato2)
#> PSwght>
#> PSwght>
#> PSwght>
#> PSwght>These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.