Last data update: 2014.03.03
R: Bootstraping for propensity score analysis
Bootstraping for propensity score analysis
Description
Bootstrapping has become a popular resampling method for estimating sampling
distributions. And propensity score analysis (PSA) has become popular for estimating
causal effects in observational studies. This function implements bootstrapping
specifically for PSA. Like typical bootstrapping methods, this function estiamtes
treatment effects for M random samples. However, unlike typical bootstrap methods,
this function allows for separate sample sizes for treatment and control units.
That is, under certain circumstances (e.g. when the ratio of treatment-to-control
units is large) bootstrapping only the control units may be desirable. Additionally,
this function provides a framework to use multiple PSA methods for each bootstrap
sample.
Usage
PSAboot(Tr, Y, X, M = 100, formu = as.formula(paste0("treat ~ ",
paste0(names(X), collapse = " + "))), control.ratio = 5,
control.sample.size = min(control.ratio * min(table(Tr)), max(table(Tr))),
control.replace = TRUE, treated.sample.size = min(table(Tr)),
treated.replace = TRUE, methods = getPSAbootMethods(), parallel = TRUE,
seed = NULL, ...)
Arguments
Tr
numeric (0 or 1) or logical vector of treatment indicators.
Y
vector of outcome varaible.
X
matrix or data frame of covariates used to estimate the propensity scores.
M
number of bootstrap samples to generate.
formu
formula used for estimating propensity scores. The default is to use
all covariates in X.
control.ratio
the ratio of control units to sample relative to the treatment units.
control.sample.size
the size of each bootstrap sample of control units.
control.replace
whether to use replacement when sampling from control units.
treated.sample.size
the size of each bootstrap sample of treatment units. The
default uses all treatment units for each boostrap sample.
treated.replace
whether to use replacement when sampling from treated units.
methods
a named vector of functions for each PSA method to use.
parallel
whether to run the bootstrap samples in parallel.
seed
random seed. Each iteration, i, will use a seed of seed + i.
...
other parameters passed to Match and psa.strata
Value
a list with following elements:
overall.summary Data frame with the results using the complete
dateset (i.e. unboostrapped results).
overall.details Objects returned from each method for complete dataset.
pooled.summary Data frame with results of each boostrap sample.
pooled.details List of objects returned from each method for each
boostrap sample.
control.sample.size sample size used for control units.
treated.sample.size sample size used for treated units.
control.replace whether control units were sampled with replacement.
treated.replace whether treated units were sampled with replacement.
Tr vector of treatment assignment.
Y vector out outcome.
X matrix or data frame of covariates.
M number of bootstrap samples.
See Also
getPSAbootMethods
Results
providing 
.