This packages provides functions to estimate and visualize multilevel propensity score analysis.
This package extends the principles put forth by the PSAgraphics
(Helmreich, Pruzek, & Xiong, 2010) for multilevel, or clustered, data.
Propensity score analyses are typically done in two phases. In phase I, a
statistical model predicting treatment using the available individual covariates
is estimated. This package currently currently provides functions to perform
propensity score estimates using logistic regression (see mlpsa.logistic)
and conditional inference trees (see mlpsa.ctree). The latter method
provides explicit stratifications as defined by each leaf node. The former however,
results in a numerical value ranging from zero to one (i.e. the fitted values).
A common approach is to then create stratifications using quintiles. However,
other approaches such as Loess regression are also provided.
Phase II of typical propensity score analyses concerns with the comparison of an
outcome between the treatment and comparison groups. The mlpsa
method will perform this analysis in a multilevel, or clustered, fashion. That
is, the results of the mlpsa procedure produce summary results
at level one (i.e. each strata within each cluster), level two (i.e. overall results
for each cluster), and overall (i.e. overall results across all clusters).
This package also provides a number of visualizations that provide a critical
part in presenting, understanding, and interpreting the results. See
plot.mlpsa for details.
https://CRAN.R-project.org/package=PSAgraphics http://www.jstatsoft.org/v29/i06/
PSAgraphics