IPW-adjusted Kaplan-Meier analysis and IPW-adjusted RMST analysis after multiple imputation

Question

I would like to do the following analyses with the dataset with missing variables. Because mice and MatchThem packages do not support pooling the results of Kaplan-Meier analysis, I try to do it manually as follows:

1. Do multiple imputations using mice.
2. Calculate inverse probability weights in each imputed dataset using WeightIt.
3. Estimate IPW-adjusted Kaplan-Meier curves in each imputed dataset using survfit.
4. Pool the results of #3 and depict the pooled IPW-adjusted KM curves.
5. Calculate the difference in IPW-adjusted restricted mean survival time (the area under KM curve until the specific timepoint) according to akm-rmst (https://github.com/s-conner/akm-rmst) within each imputed dataset.
6. Pool the results of #5.
7. Get descriptive statistics of baseline characteristics in imputed dataset using tbl_summary from gtsummary package.

Here are my codes

pacman::p_load(survival, survey, survminer, WeightIt, tidyverse, mice)

df # sample dataset

m <- 10 # number of imputation

dimp <- mice::mice(df, m = m, seed = 123)

for (i in 1:m) {
  dcomp <- mice::complete(dimp, i) # extract imputed data
  
  # estimate weight
  wgt <- weightit(
    treatment ~ age + sex + smoking, 
    data = dcomp, method = "ps", estimand = "ATE", stabilize = TRUE
  )

  # add weight and pscore to dataset
  dimp <- tibble(dcomp, wgt = wgt[["weights"]], pscores = wgt[["ps"]]) 

  assign(paste0("df", i), output) # save "i"th imputed dataset

  # calculate Kaplan-Meier estimate
  surv <- survival::survfit(Surv(time, event) ~ treatment, data = dimp, weight = wgt)

  assign(paste0("surv", i), output) # save "i"th IPW-adjusted KM curves
}

These codes do the analyses from #1 to #3. Although I read the reference (https://stefvanbuuren.name/fimd/sec-pooling.html), I could not find how to do these analyses(#4 to #7). Can anyone give me some advice regarding #4 to #7?

I believe this is not a duplicate to any posted question so I'd appreciate any advice. Any assistance you can provide would be greatly appreciated.