Glance at a pooled inverse probability weighted result

glance() describes a pool_ipw() result rather than its estimates: one row naming the estimand, counting the observations and the results that were pooled, and reporting the complete-data degrees of freedom the small-sample adjustment used. A pooled result reporting several effect measures, or several contrasts of a categorical exposure, still returns exactly one row.

Usage

# S3 method for class 'ipw_pooled'
glance(x, ...)

Arguments

x

An ipw_pooled object, as returned by pool_ipw().

...

These dots are for future extensions and must be empty.

Value

A one-row tibble with the columns:

estimand

The causal estimand every pooled result targeted.

nobs

The smallest number of observations any pooled result was estimated from. The imputed datasets are the same size, so this is that size unless a result was fitted on a subset of one. Reported by stats::nobs().

m

The number of results pooled.

dfcom

The complete-data degrees of freedom the Barnard-Rubin adjustment used.

See also

pool_ipw() for the pooling, tidy() for the pooled estimates, and the Multiple imputation section of ipw() for the workflow the two belong to.

Examples

set.seed(2024)
n <- 150
x1 <- rnorm(n)
z <- rbinom(n, 1, plogis(0.3 * x1))
y <- rbinom(n, 1, plogis(-0.4 + 0.9 * z + 0.5 * x1))
dat <- data.frame(x1, z, y)
dat$x1[rbinom(n, 1, 0.2) == 1] <- NA

imp <- mice::mice(dat, m = 2, print = FALSE, seed = 1)
fits <- with(imp, {
  ps <- glm(z ~ x1, family = binomial())
  w <- wt_ate(ps)
  om <- glm(y ~ z, family = quasibinomial(), weights = w)
  ipw(ps, om)
})
#> ℹ Using exposure variable "z" from GLM model
#> ℹ Treating `.exposure` as binary
#> ℹ Using exposure variable "z" from GLM model
#> ℹ Treating `.exposure` as binary

glance(pool_ipw(fits))
#> # A tibble: 1 × 4
#>   estimand  nobs     m dfcom
#>   <chr>    <int> <int> <dbl>
#> 1 ate        150     2   141