Balance tables for psave objects

A method for cobalt::bal.tab(): assesses balance on the covariates of a psave() fit under the implied inverse-probability weights, with the model-averaged propensity score and (when available) the model-averaged prognostic score supplied as distance measures – the prognostic-score balance diagnostic of Stuart, Lee and Leacy (2013).

Usage

# S3 method for class 'psave'
bal.tab(x, ...)

Arguments

x: A psave object.
...: Further arguments passed on to cobalt::bal.tab() (e.g., un = TRUE, thresholds = c(m = 0.1)).

Value

A bal.tab object; see cobalt::bal.tab().

Details

The call delegates to the default cobalt machinery as cobalt::bal.tab(<covariates>, treat = x$treat, weights = x$weights, s.d.denom = x$s.d.denom, distance = data.frame(ps = x$ps, prog = x$prog), ...), so all the usual cobalt arguments (un, stats, thresholds, ...) are available, and display conventions are cobalt's own (the selection criterion inside psave() uses the paper's uniform sample-SD standardization instead; see psave_criteria()).

References

Stuart EA, Lee BK, Leacy FP (2013). Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research. Journal of Clinical Epidemiology, 66(8), S84-S90. doi:10.1016/j.jclinepi.2013.01.013

Examples

data("lalonde", package = "MatchIt")
fit <- psave(treat ~ age + educ + married + re74, data = lalonde,
             outcome = ~ re78, ps.methods = "glm", prog.methods = "glm")
cobalt::bal.tab(fit, un = TRUE)
#> Balance Measures
#>             Type Diff.Un Diff.Adj
#> ps      Distance  0.9447   0.0394
#> prog    Distance -0.6949  -0.0340
#> age      Contin. -0.3094   0.0402
#> educ     Contin.  0.0550  -0.0220
#> married   Binary -0.3236   0.0008
#> re74     Contin. -0.7211  -0.0271
#> 
#> Effective sample sizes
#>            Control Treated
#> Unadjusted  429.       185
#> Adjusted    278.11     185