compute_sub_smd: Compute standardized mean differences for subclassification result

Description

This function accepts a MatchIt object (i.e., the result of matchit function) , and calculates the overall standardized mean difference after subclassification. Note only subclassification result is applicable to this function. For other matching results except for exact matching, use compute_smd() instead. In addition, SMD can be calculated on the basis of the standard deviation of original treatment group, which is the formula used in matchit function, or on the basis of the simple pooled standard deviation of original treatment and control group. The default is sd = "pooled", but it can be switched to "treatment".

Usage

compute_sub_smd(mi_obj = NULL, sd = "pooled")

Arguments

mi_obj

A matchit object derived from MatchIt pacakge

The standard deviation used as the denominator in the formula

Value

Return a scalar (the overall SMD)

References

Austin, P. C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46(3), 399-424. https://doi.org/10.1080/00273171.2011.568786

Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011). MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. Journal of Statistical Software, 42(8). https://doi.org/10.18637/jss.v042.i08

Examples

Run this code

# NOT RUN {
# take lalonde data as an example
# run matchit() to obtain the matching result
 m_out <- MatchIt::matchit(treat ~ re74 + re75 + age + educ + hispan +
   black, data = MatchIt::lalonde, method = "subclass", subclass = 7)
 compute_sub_smd(m_out, sd = "treatment")
# }