Bootstrapping for Propensity Score Analysis
Convert the results of PSAboot summary to a data frame.
Returns balance for each covariate from propensity score matching.
Returns a summary of the balance for all bootstrap samples.
Stratification using classification trees for bootstrapping.
Matching package implementation for bootstrapping.
MatchIt package implementation for bootstrapping.
Stratification using classification trees for bootstrapping.
Stratification implementation for bootstrapping.
Propensity score weighting implementation for bootstrapping.
Boxplot of the balance statistics for bootstrapped samples.
Boxplot of PSA bootstrap results.
Calculates propensity score weights.
Histogram of PSA bootstrap results
Matrix Plot of Bootstrapped Propensity Score Analysis
Plot method for balance.
Plot the results of PSAboot
Print method for balance.
Print results of PSAboot
Print method for PSAboot Summary.
Propensity Score Analysis using Stratification
Bootstrapping for Propensity Score Analysis
Returns a vector with the default methods used by PSAboot().
Return the 25th percentile.
Returns the 75th percentile.
Summary of pooled results from PSAboot
It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) <doi:10.1093/biomet/ass032>. The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using 'rpart' and 'ctree' functions), two matching methods (using 'Matching' and 'MatchIt' packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results.