Updating README intro, simulation results table labels.

DESCRIPTION

@@ -15,7 +15,7 @@ Authors@R: c(
     person(given = "Xu", family = "Shi",
            email = "shixu@umich.edu", role = c("aut")))
 Maintainer: Stephen Salerno <ssalerno@fredhutch.org>
-Description: Estimates the population average controlled difference for a given outcome between levels of a binary treatment, exposure, or other group membership variable of interest for clustered, stratified survey samples where sample selection depends on the comparison group. Provides three methods for estimation, namely outcome modeling and two factorizations of inverse probability weighting. Under stronger assumptions, these methods estimate the causal population average treatment effect. Salerno et al., (2024) <doi:10.48550/arXiv.2406.19597> 
+Description: Estimates the population average controlled difference for a given outcome between levels of a binary treatment, exposure, or other group membership variable of interest for clustered, stratified survey samples where sample selection depends on the comparison group. Provides three methods for estimation, namely outcome modeling and two factorizations of inverse probability weighting. Under stronger assumptions, these methods estimate the causal population average treatment effect. Salerno et al., (2024) <doi:10.48550/arXiv.2406.19597>. 
 License: GPL (>= 3)
 Encoding: UTF-8
 LazyData: true

README.Rmd

@@ -19,7 +19,7 @@ knitr::opts_chunk$set(
 [![R-CMD-check](https://github.com/salernos/svycdiff/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/salernos/svycdiff/actions/workflows/R-CMD-check.yaml)
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-A basic descriptive question in statistics asks whether there are differences in mean outcomes between groups based on levels of a discrete covariate (e.g., racial disparities in health outcomes, differences in opinion based on political party identification, heterogeneity in educational outcomes for students in urban vs. rural school districts, etc).  When this categorical covariate of interest is correlated with other factors related to the outcome, however, direct comparisons may lead to erroneous estimates and invalid inferential conclusions without appropriate adjustment. 
+Propensity score methods are broadly employed with observational data as a tool to achieve covariate balance, but how to implement them in complex surveys is less studied -- in particular, when the survey weights depend on the group variable under comparison.
 
 
 In this package, we focus on the specific case when sample selection depends the comparison groups of interest. We implement identification formulas to properly estimate the *average controlled difference (ACD)*, or under stronger assumptions, the *population average treatment effect (ATE)* in outcomes between groups, with appropriate weighting for covariate imbalance and generalizability. 

README.md

@@ -8,15 +8,10 @@
 [![R-CMD-check](https://github.com/salernos/svycdiff/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/salernos/svycdiff/actions/workflows/R-CMD-check.yaml)
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-A basic descriptive question in statistics asks whether there are
-differences in mean outcomes between groups based on levels of a
-discrete covariate (e.g., racial disparities in health outcomes,
-differences in opinion based on political party identification,
-heterogeneity in educational outcomes for students in urban vs. rural
-school districts, etc). When this categorical covariate of interest is
-correlated with other factors related to the outcome, however, direct
-comparisons may lead to erroneous estimates and invalid inferential
-conclusions without appropriate adjustment.
+Propensity score methods are broadly employed with observational data as
+a tool to achieve covariate balance, but how to implement them in
+complex surveys is less studied – in particular, when the survey weights
+depend on the group variable under comparison.
 
 In this package, we focus on the specific case when sample selection
 depends the comparison groups of interest. We implement identification