Propensity Score Matching in Stata

Propensity Score Matching in Stata
Chapter 2: STATA Code
Sample dataset codebook:
treat = Binary indicator of treatment versus control group
x1-x5 = continuous confounders associated with Treat
cont_out = Continuous outcome of interest
bin_out = Binary outcome of interest
Estimating the propensity score in STATA with logistic regression
STATA> logistic treat x1 x2 x3 x4 x5
STATA> predict pscore
 
MATCHING USING PSMATCH2 PACKAGE
// Install psmatch2.ado file
STATA> findit psmatch2
// Sort individuals randomly before matching
// Set random seed prior to psmatch2 to ensure replication
STATA> set seed 1234
STATA> generate sort_id = uniform()
STATA> sort sort_id
 
K:1 matching, with and without replacement
// 1:1 matching with replacement, estimate PS with logistic regression
STATA> psmatch2 treat x1 x2 x3 x4 x5, logit
// 2:1 matching without replacement
STATA> psmatch2 treat x1 x2 x3 x4 x5, logit noreplace n(2)
// 2:1 matching with replacement and caliper, PS previously estimated
STATA> psmatch2 treat, pscore(pscore) n(2) cal(0.20)
// Mahalanobis matching with caliper
STATA> psmatch2 treat, mahal(x1 x2 x3 x4 x5) cal(0.10)
Radius matching
STATA> psmatch2 treat x1 x2 x3 x4 x5, logit radius caliper(0.10)
 
Kernel matching
// Kernel matching, PS estimated with logistic regression
STATA> psmatch2 treat x1 x2 x3 x4 x5, kernel logit
// Perform kernel matching, bandwidth=0.10
STATA> psmatch2 treat x1 x2 x3 x4 x5, kernel logit bwidth(0.10)
// Estimate ATT for outcome variable(s)
STATA> psmatch2 treat x1 x2 x3 x4 x5, kernel outcome(cont_out)
Effect estimates
// ATT (default estimand) for both outcome variables
STATA> psmatch2 treat x1 x2 x3 x4 x5, outcome(cont_out bin_out) logit
// Regression approach: Equivalent to ATT estimates from psmatch2
// First generate weights from psmatch2
STATA> psmatch2 treat x1 x2 x3 x4 x5, logit
STATA> regress cont_out treat [iweight=_weight] if _weight!=.
// Regression including covariates
STATA> regress cont_out treat x1 x2 x3 x4 x5 [iweight=_weight] if _weight!=.
Balance diagnostics
// Balance table and plot
STATA> pstest x1 x2 x3 x4 x5, both graph
MATCHING USING TEFFECTS (STATA 13)
Nearest neighbor matching
// 1:1 Nearest Neighbor Matching with replacement, estimate ATT effect
STATA> teffects psmatch (cont_out)(treat x1 x2 x3 x4 x5), nn(1) atet
// 2:1 Nearest Neighbor Matching with replacement, estimate ATT effect
STATA> teffects psmatch (cont_out)(treat x1 x2 x3 x4 x5), nn(2) atet
 
Mahalanobis matching
// 1:1 Mahalanobis matching, ATT effect
STATA> teffects nnmatch (cont_out x1 x2 x3 x4 x5) (treat), atet
MATCHING USING CEM PACKAGE
Coarsened exact matching
/* install cem package
STATA> findit cem
/* CEM with automatic binning
STATA> cem x1 x2 x3 x4 x5, treatment(treat)
/* CEM with user-specified cutpoints for x3
STATA> cem x1 x2 x3 (0 1.5 4 7 9 14) x4 x5, treatment(treat)
/* Estimate treatment effects using weights since variable-ratio
STATA> regress cont_out treat x1 x2 x3 x4 x5 [iweight=cem_weights]
/* Restrict so that all strata contain the same number of treated and controls; no weights necessary in final analysis
STATA> cem x1 x2 x3 x4 x5, treatment(treat) k2k
/* Estimate treatment effects; no weighting
STATA> regress cont_out treat x1 x2 x3 x4 x5
 
PROPENSITY SCORE WEIGHTING, PARAMETRIC PS ESTIMATION
// Estimate the propensity score with logistic regression
STATA> logistic treat x1 x2 x3 x4 x5
STATA> predict pscore
// Calculate ATE propensity score weights (IPTW)
STATA> gen w_ate = treat/pscore + (1-treat)/(1-pscore)
// Use ATE weights as probability weights in final analysis
STATA> svyset [pw=w_ate]
STATA> svy: regress cont_out treat x1 x2 x3 x4 x5
 
// Calculate ATT propensity score weights
STATA> gen w_att = treat + (1-treat)*(pscore/(1-pscore))
// Use ATT weights as probability weights in final analysis
STATA> svyset [pw=w_att]
STATA> svy: regress cont_out treat x1 x2 x3 x4 x5
SUBCLASSIFICATION
Creating 5 propensity score subclasses
// After generating propensity score, can create quintiles
STATA> xtile pscore_5 = pscore, nq(5)
 
Estimating subclass-specific and overall effect estimates
// Binary outcome: Mantel-Haenszel stratified analysis
STATA> cc bin_out treat, by(pscore_5) bd
// Continuous outcome: Van Elteren test (Stratified Wilcoxon rank sum)
/* install Van Elteren package
STATA> findit vanelteren
STATA> vanelteren cont_out, by(treat) strata(pscore_5)
 

admin

Author Since: November 30, 2020