Updated readme.

README.Rmd

@@ -29,9 +29,7 @@ Bookdown Site: https://psa.bryer.org
 
 ## Overview
 
-The use of propensity score methods (Rosenbaum & Rubin, 1983) for estimating causal effects in observational studies or certain kinds of quasi-experiments has been increasing in the social sciences (Thoemmes & Kim, 2011) and in medical research (Austin, 2008) in the last decade. Propensity score analysis (PSA) attempts to adjust selection bias that occurs due to the lack of randomization. Analysis is typically conducted in two phases where in phase I, the probability of placement in the treatment is estimated to identify matched pairs or clusters so that in phase II, comparisons on the dependent variable can be made between matched pairs or within clusters. R (R Core Team, 2012) is ideal for conducting PSA given its wide availability of the most current statistical methods vis-à-vis add-on packages as well as its superior graphics capabilities.
-
-This workshop will provide participants with a theoretical overview of propensity score methods as well as illustrations and discussion of PSA applications. Methods used in phase I of PSA (i.e. models or methods for estimating propensity scores) include logistic regression, classification trees, and matching. Discussions on appropriate comparisons and estimations of effect size and confidence intervals in phase II will also be covered. The use of graphics for diagnosing covariate balance as well as summarizing overall results will be emphasized. 
+The use of propensity score methods (Rosenbaum & Rubin, 1983) for estimating causal effects in observational studies or certain kinds of quasi-experiments has been increasing over the last two decades. Propensity score analysis (PSA) attempts to adjust selection bias that occurs due to the lack of randomization. Analysis is typically conducted in three phases. In phase I, the probability of placement in the treatment is estimated to identify matched pairs, clusters, or probability weights. In phase II, comparisons on the dependent variable can be made between matched pairs, within clusters, or using inverse probability weights in regression models. In phase III, sensitivity analysis is conducted to estimate how robust the effect sizes estimated in phase II are to unobserved confounders. R (R Core Team, 2012) is ideal for conducting PSA given its wide availability of the most current statistical methods vis-à-vis add-on packages as well as its superior graphics capabilities. This talk will provide participants with a theoretical overview of propensity score methods with an emphasis on graphics. A survey of R packages for conducting PSA with multilevel data, non-binary treatments, and bootstrapping will also be provided. Lastly, a Shiny application to assist with all three phases of PSA will be demonstrated.
 
 ```{r psa_citations_by_year, echo=FALSE, fig.width=10, fig.height=4}
 data('psa_citations', package = 'psa')

README.md

@@ -6,7 +6,7 @@
 [![R-CMD-check](https://github.com/jbryer/psa/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/jbryer/psa/actions/workflows/R-CMD-check.yaml)
 [![Bookdown
 Status](https://github.com/jbryer/psa/actions/workflows/bookdown.yaml/badge.svg)](https://github.com/jbryer/psa/actions/workflows/bookdown.yaml)
-[![](https://img.shields.io/badge/devel%20version-0.1.0-blue.svg)](https://github.com/jbryer/psa)
+[![](https://img.shields.io/badge/devel%20version-0.1.1-blue.svg)](https://github.com/jbryer/psa)
 [![Project Status: WIP - Initial development is in progress, but there
 has not yet been a stable, usable release suitable for the
 public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
@@ -19,27 +19,24 @@ Bookdown Site: <https://psa.bryer.org>
 
 The use of propensity score methods (Rosenbaum & Rubin, 1983) for
 estimating causal effects in observational studies or certain kinds of
-quasi-experiments has been increasing in the social sciences (Thoemmes &
-Kim, 2011) and in medical research (Austin, 2008) in the last decade.
+quasi-experiments has been increasing over the last two decades.
 Propensity score analysis (PSA) attempts to adjust selection bias that
 occurs due to the lack of randomization. Analysis is typically conducted
-in two phases where in phase I, the probability of placement in the
-treatment is estimated to identify matched pairs or clusters so that in
-phase II, comparisons on the dependent variable can be made between
-matched pairs or within clusters. R (R Core Team, 2012) is ideal for
-conducting PSA given its wide availability of the most current
+in three phases. In phase I, the probability of placement in the
+treatment is estimated to identify matched pairs, clusters, or
+probability weights. In phase II, comparisons on the dependent variable
+can be made between matched pairs, within clusters, or using inverse
+probability weights in regression models. In phase III, sensitivity
+analysis is conducted to estimate how robust the effect sizes estimated
+in phase II are to unobserved confounders. R (R Core Team, 2012) is
+ideal for conducting PSA given its wide availability of the most current
 statistical methods vis-à-vis add-on packages as well as its superior
-graphics capabilities.
-
-This workshop will provide participants with a theoretical overview of
-propensity score methods as well as illustrations and discussion of PSA
-applications. Methods used in phase I of PSA (i.e. models or methods for
-estimating propensity scores) include logistic regression,
-classification trees, and matching. Discussions on appropriate
-comparisons and estimations of effect size and confidence intervals in
-phase II will also be covered. The use of graphics for diagnosing
-covariate balance as well as summarizing overall results will be
-emphasized.
+graphics capabilities. This talk will provide participants with a
+theoretical overview of propensity score methods with an emphasis on
+graphics. A survey of R packages for conducting PSA with multilevel
+data, non-binary treatments, and bootstrapping will also be provided.
+Lastly, a Shiny application to assist with all three phases of PSA will
+be demonstrated.
 
 <img src="man/figures/README-psa_citations_by_year-1.png" width="100%" />