rsample_ipw

library(tidyverse)
library(broom)
library(rsample)
# remotes::install_github("malcolmbarrett/cidata")
library(cidata)

# fit ipw model for a single bootstrap sample
fit_ipw <- function(split, ...) {
  # get bootstrapped data sample
  .df <- analysis(split)
  
  # fit propensity score model
  propensity_model <- glm(
    qsmk ~ sex + 
      race + age + I(age^2) + education + 
      smokeintensity + I(smokeintensity^2) + 
      smokeyrs + I(smokeyrs^2) + exercise + active + 
      wt71 + I(wt71^2), 
    family = binomial(), 
    data = .df
  )
  
  # calculate inverse probability weights
  .df <- propensity_model %>% 
    augment(type.predict = "response", data = .df) %>% 
    mutate(wts = 1 / ifelse(qsmk == 0, 1 - .fitted, .fitted))
  
  # fit ipw model
  lm(wt82_71 ~ qsmk, data = .df, weights = wts) %>% 
    tidy()
}

# fit ipw model to bootstrapped samples
ipw_results <- bootstraps(nhefs_complete, 1000, apparent = TRUE) %>% 
  mutate(results = map(splits, fit_ipw))

# get t-statistic-based CIs
int_t(ipw_results, results)