diff --git a/DESCRIPTION b/DESCRIPTION
index f04bc9d1..1b6cac16 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,8 +22,7 @@ Imports:
     MASS,
     boot,
     stringr,
-    lmtest,
-    cli
+    lmtest
 Suggests:
     knitr,
     testthat (>= 2.0),
diff --git a/R/maic_anchored.R b/R/maic_anchored.R
index 6199a987..fefc0470 100644
--- a/R/maic_anchored.R
+++ b/R/maic_anchored.R
@@ -19,6 +19,8 @@
 #' @param time_scale a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or
 #'   'days'. NOTE: it is assumed that values in TIME column of \code{ipd} and \code{pseudo_ipd} is in the unit of days
 #' @param km_conf_type a string, pass to \code{conf.type} of \code{survfit}
+#' @param boot_ci_type a string, one of `c("norm","basic", "stud", "perc", "bca")` to select the type of bootstrap
+#'   confidence interval. See [boot::boot.ci] for more details.
 #'
 #' @details For time-to-event analysis, it is required that input \code{ipd} and \code{pseudo_ipd} to have the following
 #'   columns. This function is not sensitive to upper or lower case of letters in column names.
@@ -47,7 +49,8 @@ maic_anchored <- function(weights_object,
                           eff_measure = c("HR", "OR", "RR", "RD", "Diff"),
                           # time to event specific args
                           time_scale = "months",
-                          km_conf_type = "log-log") {
+                          km_conf_type = "log-log",
+                          boot_ci_type = c("norm", "basic", "stud", "perc", "bca")) {
   # ==> Initial Setup ------------------------------------------
   # create the hull for the output from this function
   res <- list(
@@ -114,6 +117,7 @@ maic_anchored <- function(weights_object,
     }
     eff_measure <- match.arg(eff_measure, choices = c("HR"), several.ok = FALSE)
   }
+  boot_ci_type <- match.arg(boot_ci_type)
   # else { # for continuous effect measure
   #   if (any(!c("USUBJID", "RESPONSE") %in% names(ipd))) {
   #   stop("ipd should have 'USUBJID', 'RESPONSE' columns at minimum")
@@ -151,7 +155,10 @@ maic_anchored <- function(weights_object,
 
   # ==> Inferential output ------------------------------------------
   result <- if (endpoint_type == "tte") {
-    maic_anchored_tte(res, ipd, km_conf_type, pseudo_ipd, time_scale, weights_object, endpoint_name, trt_ipd, trt_agd)
+    maic_anchored_tte(
+      res, ipd, km_conf_type, pseudo_ipd, time_scale, weights_object, endpoint_name, trt_ipd, trt_agd,
+      boot_ci_type
+    )
   } else {
     stop("Endpoint type ", endpoint_type, " currently unsupported.")
   }
@@ -170,7 +177,8 @@ maic_anchored_tte <- function(res,
                               weights_object,
                               endpoint_name,
                               trt_ipd,
-                              trt_agd) {
+                              trt_agd,
+                              boot_ci_type) {
   # Analysis table (Cox model) before and after matching, incl Median Survival Time
   # derive km w and w/o weights
   kmobj_ipd <- survfit(Surv(TIME, EVENT) ~ ARM, ipd, conf.type = km_conf_type)
@@ -208,20 +216,58 @@ maic_anchored_tte <- function(res,
   # get bootstrapped estimates if applicable
   res$inferential[["boot_est"]] <- NULL
   if (!is.null(weights_object$boot)) {
-    tmp_boot_obj <- weights_object$boot
-    k <- dim(tmp_boot_obj)[3]
-    tmp_boot_est <- sapply(seq_len(k), function(ii) {
-      boot_x <- tmp_boot_obj[, , ii]
-      boot_ipd_id <- weights_object$data$USUBJID[boot_x[, 1]]
-      boot_ipd <- ipd[match(boot_ipd_id, ipd$USUBJID), , drop = FALSE]
-      boot_ipd$weights <- boot_x[, 2]
+    keep_rows <- setdiff(seq_len(nrow(weights_object$data)), weights_object$rows_with_missing)
+    boot_ipd_id <- weights_object$data[keep_rows, "USUBJID", drop = FALSE]
+
+    boot_ipd <- merge(boot_ipd_id, ipd, by = "USUBJID", all.x = TRUE)
+    if (nrow(boot_ipd) != nrow(boot_ipd_id)) stop("ipd has multiple observations for some patients")
+    boot_ipd <- boot_ipd[match(boot_ipd$USUBJID, boot_ipd_id$USUBJID), ]
+
+    stat_fun <- function(data, index, w_obj) {
+      r <- dynGet("r", ifnotfound = NA) # Get bootstrap iteration
+      if (!is.na(r)) {
+        if (!all(index == w_obj$boot[, 1, r])) stop("Bootstrap and weight indices don't match")
+        boot_ipd <- data[w_obj$boot[, 1, r], ]
+        boot_ipd$weights <- w_obj$boot[, 2, r]
+      }
+      boot_coxobj_dat_adj <- coxph(Surv(TIME, EVENT) ~ ARM, boot_ipd, weights = boot_ipd$weights, robust = TRUE)
+      boot_res_AC <- list(est = coef(boot_coxobj_dat_adj)[1], se = sqrt(vcov(boot_coxobj_dat_adj)[1, 1]))
+      boot_res_AB <- bucher(boot_res_AC, res_BC, conf_lv = 0.95)
+      c(
+        est_AB = boot_res_AB$est,
+        var_AB = boot_res_AB$se^2,
+        se_AB = boot_res_AB$se,
+        est_AC = boot_res_AC$est,
+        se_AC = boot_res_AC$se,
+        var_AC = boot_res_AC$se^2
+      )
+    }
 
-      # does not matter use robust se or not, point estimate will not change and calculation would be faster
-      boot_coxobj_ipd_adj <- coxph(Surv(TIME, EVENT) ~ ARM, boot_ipd, weights = weights)
-      boot_AB_est <- summary(boot_coxobj_ipd_adj)$coef[1] - summary(coxobj_agd)$coef[1]
-      exp(boot_AB_est)
-    })
-    res$inferential[["boot_est"]] <- tmp_boot_est
+    # Revert seed to how it was for weight bootstrap sampling
+    old_seed <- globalenv()$.Random.seed
+    on.exit(suspendInterrupts(set_random_seed(old_seed)))
+    set_random_seed(weights_object$boot_seed)
+
+    R <- dim(weights_object$boot)[3]
+    boot_res <- boot(boot_ipd, stat_fun, R = R, w_obj = weights_object, strata = weights_object$boot_strata)
+    boot_ci <- boot.ci(boot_res, type = boot_ci_type, w_obj = weights_object)
+
+    l_u_index <- switch(boot_ci_type,
+      "norm" = list(2, 3, "normal"),
+      "basic" = list(4, 5, "basic"),
+      "stud" = list(4, 5, "student"),
+      "perc" = list(4, 5, "percent"),
+      "bca" = list(4, 5, "bca")
+    )
+
+    res$inferential[["boot_est"]] <- boot_res
+    boot_res_AB <- list(
+      est = exp(boot_res$t0[1]),
+      se = NA,
+      ci_l = exp(boot_ci[[l_u_index[[3]]]][l_u_index[[1]]]),
+      ci_u = exp(boot_ci[[l_u_index[[3]]]][l_u_index[[2]]]),
+      pval = NA
+    )
   }
 
   # make analysis report table
@@ -239,10 +285,11 @@ maic_anchored_tte <- function(res,
   if (is.null(res$inferential[["boot_est"]])) {
     res$inferential[["report_overall_bootCI"]] <- NULL
   } else {
-    boot_res_AB <- res_AB
-    boot_logres_se <- sd(log(res$inferential[["boot_est"]]), na.rm = TRUE)
-    boot_res_AB$ci_l <- exp(log(boot_res_AB$est) + qnorm(0.025) * boot_logres_se)
-    boot_res_AB$ci_u <- exp(log(boot_res_AB$est) + qnorm(0.975) * boot_logres_se)
+    temp_boot_res <- boot_res_AB
+    temp_boot_res$ci_l <- boot_res_AB$ci_l
+    temp_boot_res$ci_u <- boot_res_AB$ci_u
+    class(temp_boot_res) <- class(res_AB)
+
     res$inferential[["report_overall_bootCI"]] <- rbind(
       report_table_tte(coxobj_ipd, medSurv_ipd, tag = paste0("IPD/", endpoint_name)),
       report_table_tte(coxobj_ipd_adj, medSurv_ipd_adj, tag = paste0("weighted IPD/", endpoint_name)),
@@ -250,7 +297,7 @@ maic_anchored_tte <- function(res,
       c(
         paste0("** adj.", trt_ipd, " vs ", trt_agd),
         rep("-", 4),
-        print(boot_res_AB, pval_digits = 3)
+        print(temp_boot_res, pval_digits = 3)
       )
     )
   }
diff --git a/R/maic_unanchored.R b/R/maic_unanchored.R
index 7e653fcb..28df962e 100644
--- a/R/maic_unanchored.R
+++ b/R/maic_unanchored.R
@@ -14,16 +14,14 @@
 #' @param endpoint_type a string, one out of the following "binary", "tte" (time to event)
 #' @param eff_measure a string, "RD" (risk difference), "OR" (odds ratio), "RR" (relative risk) for a binary endpoint;
 #'   "HR" for a time-to-event endpoint. By default is \code{NULL}, "OR" is used for binary case, otherwise "HR" is used.
-#' @param boot_ci_is_quantile a logical, specify if the 95% bootstrapped confidence interval should be derived by sample
-#'   quantile. Default FALSE, which the estimates assumes to follow asymptotic normal (only if `eff_measure` is "RD") or
-#'   log-normal with a variance that can be approximated by bootstrapped sample of the estimate. This default option may
-#'   be handy when the number of bootstrap iterations is not big.
+#' @param boot_ci_type a string, one of `c("norm","basic", "stud", "perc", "bca")` to select the type of bootstrap
+#'   confidence interval. See [boot::boot.ci] for more details.
 #' @param endpoint_name a string, name of time to event endpoint, to be show in the last line of title
 #' @param time_scale a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or
 #'   'days'. NOTE: it is assumed that values in TIME column of \code{ipd} and \code{pseudo_ipd} is in the unit of days
 #' @param km_conf_type a string, pass to \code{conf.type} of \code{survfit}
-#' @param binary_robust_cov_type a string to pass to argument `type` of [sandwich::vcovHC], see possible options
-#'   in the documentation of that function. Default is `"HC3"`
+#' @param binary_robust_cov_type a string to pass to argument `type` of [sandwich::vcovHC], see possible options in the
+#'   documentation of that function. Default is `"HC3"`
 #'
 #' @details For time-to-event analysis, it is required that input \code{ipd} and \code{pseudo_ipd} to have the following
 #'   columns. This function is not sensitive to upper or lower case of letters in column names.
@@ -49,7 +47,7 @@ maic_unanchored <- function(weights_object,
                             endpoint_type = "tte",
                             endpoint_name = "Time to Event Endpoint",
                             eff_measure = c("HR", "OR", "RR", "RD"),
-                            boot_ci_is_quantile = FALSE,
+                            boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
                             # time to event specific args
                             time_scale = "months",
                             km_conf_type = "log-log",
@@ -126,6 +124,7 @@ maic_unanchored <- function(weights_object,
     }
     eff_measure <- match.arg(eff_measure, choices = c("HR"), several.ok = FALSE)
   }
+  boot_ci_type <- match.arg(boot_ci_type)
 
   # ==> IPD and AgD data preparation ------------------------------------------
   # : subset ipd, retain only ipd from interested trts
@@ -172,12 +171,12 @@ maic_unanchored <- function(weights_object,
   result <- if (endpoint_type == "tte") {
     maic_unanchored_tte(
       res, res_AB, dat, ipd, pseudo_ipd, km_conf_type, time_scale,
-      weights_object, endpoint_name, boot_ci_is_quantile, trt_ipd, trt_agd
+      weights_object, endpoint_name, boot_ci_type, trt_ipd, trt_agd
     )
   } else if (endpoint_type == "binary") {
     maic_unanchored_binary(
       res, res_AB, dat, ipd, pseudo_ipd, binary_robust_cov_type,
-      weights_object, endpoint_name, eff_measure, boot_ci_is_quantile, trt_ipd, trt_agd
+      weights_object, endpoint_name, eff_measure, boot_ci_type, trt_ipd, trt_agd
     )
   } else {
     stop("Endpoint type ", endpoint_type, " currently unsupported.")
@@ -198,7 +197,7 @@ maic_unanchored_tte <- function(res,
                                 time_scale,
                                 weights_object,
                                 endpoint_name,
-                                boot_ci_is_quantile,
+                                boot_ci_type,
                                 trt_ipd,
                                 trt_agd) {
   # ~~~ Descriptive table before and after matching
@@ -233,30 +232,52 @@ maic_unanchored_tte <- function(res,
 
   # : get bootstrapped estimates if applicable
   if (!is.null(weights_object$boot)) {
-    tmp_boot_obj <- weights_object$boot
-    k <- dim(tmp_boot_obj)[3]
-
-    cli::cli_progress_bar("Going through bootstrapped weights", total = k, .envir = .GlobalEnv)
-
-    tmp_boot_est <- sapply(1:k, function(ii) {
-      cli::cli_progress_update(.envir = .GlobalEnv)
-
-      boot_x <- tmp_boot_obj[, , ii]
-      boot_ipd_id <- weights_object$data$USUBJID[boot_x[, 1]]
-      boot_ipd <- ipd[match(boot_ipd_id, ipd$USUBJID), , drop = FALSE]
-      boot_ipd$weights <- boot_x[, 2]
-
+    keep_rows <- setdiff(seq_len(nrow(weights_object$data)), weights_object$rows_with_missing)
+    boot_ipd_id <- weights_object$data[keep_rows, "USUBJID", drop = FALSE]
+
+    boot_ipd <- merge(boot_ipd_id, ipd, by = "USUBJID", all.x = TRUE)
+    if (nrow(boot_ipd) != nrow(boot_ipd_id)) stop("ipd has multiple observations for some patients")
+    boot_ipd <- boot_ipd[match(boot_ipd$USUBJID, boot_ipd_id$USUBJID), ]
+
+    stat_fun <- function(data, index, w_obj, pseudo_ipd) {
+      boot_ipd <- data[index, ]
+      r <- dynGet("r", ifnotfound = NA) # Get bootstrap iteration
+      if (!is.na(r)) {
+        if (!all(index == w_obj$boot[, 1, r])) stop("Bootstrap and weight indices don't match")
+        boot_ipd$weights <- w_obj$boot[, 2, r]
+      }
       boot_dat <- rbind(boot_ipd, pseudo_ipd)
       boot_dat$ARM <- factor(boot_dat$ARM, levels = c(trt_agd, trt_ipd))
-
       boot_coxobj_dat_adj <- coxph(Surv(TIME, EVENT) ~ ARM, boot_dat, weights = weights)
-      boot_AB_est <- summary(boot_coxobj_dat_adj)$coef[1]
-      exp(boot_AB_est)
-    })
+      c(est = coef(boot_coxobj_dat_adj)[1], var = vcov(boot_coxobj_dat_adj)[1, 1])
+    }
 
-    cli::cli_progress_done(.envir = .GlobalEnv)
+    # Revert seed to how it was for weight bootstrap sampling
+    old_seed <- globalenv()$.Random.seed
+    on.exit(suspendInterrupts(set_random_seed(old_seed)))
+    set_random_seed(weights_object$boot_seed)
+    R <- dim(weights_object$boot)[3]
+
+    boot_res <- boot(boot_ipd, stat_fun, R = R, w_obj = weights_object, pseudo_ipd = pseudo_ipd)
+    boot_ci <- boot.ci(boot_res, type = boot_ci_type, w_obj = weights_object, pseudo_ipd = pseudo_ipd)
+
+    l_u_index <- switch(boot_ci_type,
+      "norm" = list(2, 3, "normal"),
+      "basic" = list(4, 5, "basic"),
+      "stud" = list(4, 5, "student"),
+      "perc" = list(4, 5, "percent"),
+      "bca" = list(4, 5, "bca")
+    )
 
-    res$inferential[["boot_est"]] <- tmp_boot_est
+    res$inferential[["boot_est"]] <- boot_res
+    transform_estimate <- exp
+    boot_res_AB <- list(
+      est = transform_estimate(boot_res$t0[1]),
+      se = NA,
+      ci_l = transform_estimate(boot_ci[[l_u_index[[3]]]][l_u_index[[1]]]),
+      ci_u = transform_estimate(boot_ci[[l_u_index[[3]]]][l_u_index[[2]]]),
+      pval = NA
+    )
   } else {
     res$inferential[["boot_est"]] <- NULL
   }
@@ -270,27 +291,12 @@ maic_unanchored_tte <- function(res,
   if (is.null(res$inferential[["boot_est"]])) {
     res$inferential[["report_overall_bootCI"]] <- NULL
   } else {
-    boot_res_AB <- res_AB
-    boot_logres_se <- sd(log(res$inferential[["boot_est"]]), na.rm = TRUE)
-    if (boot_ci_is_quantile) {
-      boot_res_AB$ci_l <- quantile(res$inferential[["boot_est"]], p = 0.025)
-      boot_res_AB$ci_u <- quantile(res$inferential[["boot_est"]], p = 0.975)
-    } else {
-      boot_res_AB$ci_l <- exp(log(boot_res_AB$est) + qnorm(0.025) * boot_logres_se)
-      boot_res_AB$ci_u <- exp(log(boot_res_AB$est) + qnorm(0.975) * boot_logres_se)
-    }
-    tmp_report_table_tte <- report_table_tte(coxobj_dat_adj,
-      medSurv_dat_adj,
-      tag = paste0("After matching/", endpoint_name)
-    )
-    tmp_report_table_tte$`HR[95% CI]`[1] <- paste0(
-      format(round(boot_res_AB$est, 2), nsmall = 2), "[",
-      format(round(boot_res_AB$ci_l, 2), nsmall = 2), ";",
-      format(round(boot_res_AB$ci_u, 2), nsmall = 2), "]"
-    )
+    report_boot <- report_table_tte(coxobj_dat_adj, medSurv_dat_adj, tag = paste0("After matching/", endpoint_name))
+    report_boot$`p-Value` <- NA
+    report_boot$`HR[95% CI]`[1] <- do.call(sprintf, args = c(list(fmt = "%.2f[%.2f;%.2f]"), boot_res_AB[1:3]))
     res$inferential[["report_overall_bootCI"]] <- rbind(
       report_table_tte(coxobj_dat, medSurv_dat, tag = paste0("Before matching/", endpoint_name)),
-      tmp_report_table_tte
+      report_boot
     )
   }
 
@@ -309,7 +315,7 @@ maic_unanchored_binary <- function(res,
                                    weights_object,
                                    endpoint_name,
                                    eff_measure,
-                                   boot_ci_is_quantile,
+                                   boot_ci_type,
                                    trt_ipd,
                                    trt_agd) {
   # ~~~ Analysis table
@@ -319,6 +325,11 @@ maic_unanchored_binary <- function(res,
     "RR" = poisson(link = "log"),
     "OR" = binomial(link = "logit")
   )
+  transform_estimate <- switch(eff_measure,
+    "RD" = function(x) x * 100,
+    "RR" = exp,
+    "OR" = exp
+  )
 
   # : fit glm for binary outcome and robust estimate with weights
   binobj_dat <- glm(RESPONSE ~ ARM, dat, family = glm_link)
@@ -330,57 +341,56 @@ maic_unanchored_binary <- function(res,
   res$inferential[["model_before"]] <- binobj_dat
   res$inferential[["model_after"]] <- binobj_dat_adj
 
-  mu <- bin_robust_coef[2, "Estimate"]
-  sig <- bin_robust_coef[2, "Std. Error"]
-  res_AB$ci_l <- bin_robust_ci[2, "2.5 %"]
-  res_AB$ci_u <- bin_robust_ci[2, "97.5 %"]
+  res_AB$est <- transform_estimate(bin_robust_coef[2, "Estimate"])
+  res_AB$ci_l <- transform_estimate(bin_robust_ci[2, "2.5 %"])
+  res_AB$ci_u <- transform_estimate(bin_robust_ci[2, "97.5 %"])
   res_AB$pval <- bin_robust_coef[2, "Pr(>|z|)"]
 
-  if (eff_measure %in% c("RR", "OR")) {
-    res_AB$est <- exp(mu)
-    res_AB$se <- sqrt((exp(sig^2) - 1) * exp(2 * mu + sig^2)) # log normal parameterization
-    res_AB$ci_l <- exp(res_AB$ci_l)
-    res_AB$ci_u <- exp(res_AB$ci_u)
-  } else if (eff_measure == "RD") {
-    res_AB$est <- mu * 100
-    res_AB$se <- sig * 100
-    res_AB$ci_l <- res_AB$ci_l * 100
-    res_AB$ci_u <- res_AB$ci_u * 100
-  }
-
   # : get bootstrapped estimates if applicable
   if (!is.null(weights_object$boot)) {
-    tmp_boot_obj <- weights_object$boot
-    k <- dim(tmp_boot_obj)[3]
-
-    cli::cli_progress_bar("Going through bootstrapped weights", total = k, .envir = .GlobalEnv)
-
-    tmp_boot_est <- sapply(1:k, function(ii) {
-      cli::cli_progress_update(.envir = .GlobalEnv)
-
-      boot_x <- tmp_boot_obj[, , ii]
-      boot_ipd_id <- weights_object$data$USUBJID[boot_x[, 1]]
-      boot_ipd <- ipd[match(boot_ipd_id, ipd$USUBJID), , drop = FALSE]
-      boot_ipd$weights <- boot_x[, 2]
-
+    keep_rows <- setdiff(seq_len(nrow(weights_object$data)), weights_object$rows_with_missing)
+    boot_ipd_id <- weights_object$data[keep_rows, "USUBJID", drop = FALSE]
+
+    boot_ipd <- merge(boot_ipd_id, ipd, by = "USUBJID", all.x = TRUE)
+    if (nrow(boot_ipd) != nrow(boot_ipd_id)) stop("ipd has multiple observations for some patients")
+    boot_ipd <- boot_ipd[match(boot_ipd$USUBJID, boot_ipd_id$USUBJID), ]
+
+    stat_fun <- function(data, index, w_obj, pseudo_ipd) {
+      boot_ipd <- data[index, ]
+      r <- dynGet("r", ifnotfound = NA) # Get bootstrap iteration
+      if (!is.na(r)) {
+        if (!all(index == w_obj$boot[, 1, r])) stop("Bootstrap and weight indices don't match")
+        boot_ipd$weights <- w_obj$boot[, 2, r]
+      }
       boot_dat <- rbind(boot_ipd, pseudo_ipd)
       boot_dat$ARM <- factor(boot_dat$ARM, levels = c(trt_agd, trt_ipd))
-
-      # does not matter use robust se or not, point estimate will not change and calculation would be faster
       boot_binobj_dat_adj <- glm(RESPONSE ~ ARM, boot_dat, weights = weights, family = glm_link)
-      boot_bin_robust_cov <- sandwich::vcovHC(binobj_dat_adj, type = binary_robust_cov_type)
-      boot_bin_robust_coef <- lmtest::coeftest(boot_binobj_dat_adj, vcov. = boot_bin_robust_cov)
-      boot_AB_est <- boot_bin_robust_coef[2, "Estimate"]
-      if (eff_measure %in% c("RR", "OR")) {
-        boot_AB_est <- exp(boot_AB_est)
-      } else if (eff_measure == "RD") {
-        boot_AB_est <- boot_AB_est * 100
-      }
-    })
+      c(est = coef(boot_binobj_dat_adj)[2], var = vcov(boot_binobj_dat_adj)[2, 2])
+    }
 
-    cli::cli_progress_done(.envir = .GlobalEnv)
+    # Revert seed to how it was for weight bootstrap sampling
+    old_seed <- globalenv()$.Random.seed
+    on.exit(suspendInterrupts(set_random_seed(old_seed)))
+    set_random_seed(weights_object$boot_seed)
+    R <- dim(weights_object$boot)[3]
+    boot_res <- boot(boot_ipd, stat_fun, R = R, w_obj = weights_object, pseudo_ipd = pseudo_ipd)
+    boot_ci <- boot.ci(boot_res, type = boot_ci_type, w_obj = weights_object, pseudo_ipd = pseudo_ipd)
+
+    l_u_index <- switch(boot_ci_type,
+      "norm" = list(2, 3, "normal"),
+      "basic" = list(4, 5, "basic"),
+      "stud" = list(4, 5, "student"),
+      "perc" = list(4, 5, "percent"),
+      "bca" = list(4, 5, "bca")
+    )
 
-    res$inferential[["boot_est"]] <- tmp_boot_est
+    res$inferential[["boot_est"]] <- boot_res
+    boot_res_AB <- list(
+      est = transform_estimate(boot_res$t0[1]),
+      ci_l = transform_estimate(boot_ci[[l_u_index[[3]]]][l_u_index[[1]]]),
+      ci_u = transform_estimate(boot_ci[[l_u_index[[3]]]][l_u_index[[2]]]),
+      pval = NA
+    )
   } else {
     res$inferential[["boot_est"]] <- NULL
   }
@@ -399,42 +409,22 @@ maic_unanchored_binary <- function(res,
       eff_measure = eff_measure
     )
   )
-
   if (is.null(res$inferential[["boot_est"]])) {
     res$inferential[["report_overall_bootCI"]] <- NULL
   } else {
-    boot_res_AB <- res_AB
-    boot_logres_se <- sd(log(res$inferential[["boot_est"]]), na.rm = TRUE)
-    if (boot_ci_is_quantile) {
-      boot_res_AB$ci_l <- quantile(res$inferential[["boot_est"]], p = 0.025)
-      boot_res_AB$ci_u <- quantile(res$inferential[["boot_est"]], p = 0.975)
-    } else {
-      boot_res_AB$ci_l <- exp(log(boot_res_AB$est) + qnorm(0.025) * boot_logres_se)
-      boot_res_AB$ci_u <- exp(log(boot_res_AB$est) + qnorm(0.975) * boot_logres_se)
-    }
-    tmp_report_table_binary <- report_table_binary(
-      binobj_dat_adj,
-      res_AB,
-      tag = paste0("After matching/", endpoint_name),
-      eff_measure = eff_measure
-    )
-    tmp_report_table_binary[[paste0(eff_measure, "[95% CI]")]][1] <- paste0(
-      format(round(boot_res_AB$est, 2), nsmall = 2),
-      "[",
-      format(round(boot_res_AB$ci_l, 2), nsmall = 2),
-      ";",
-      format(round(boot_res_AB$ci_u, 2), nsmall = 2),
-      "]"
-    )
     res$inferential[["report_overall_bootCI"]] <- rbind(
       report_table_binary(
         binobj_dat,
         tag = paste0("Before matching/", endpoint_name),
         eff_measure = eff_measure
       ),
-      tmp_report_table_binary
+      report_table_binary(
+        binobj_dat_adj,
+        boot_res_AB,
+        tag = paste0("After matching/", endpoint_name),
+        eff_measure = eff_measure
+      )
     )
   }
-  # output
   res
 }
diff --git a/R/matching.R b/R/matching.R
index 648b9320..fcf24f9d 100644
--- a/R/matching.R
+++ b/R/matching.R
@@ -95,6 +95,7 @@ estimate_weights <- function(data,
   EM <- data[, centered_colnames, drop = FALSE]
   ind <- apply(EM, 1, function(xx) any(is.na(xx)))
   nr_missing <- sum(ind)
+  rows_with_missing <- which(ind)
   EM <- as.matrix(EM[!ind, , drop = FALSE])
 
   # estimate weights
@@ -105,30 +106,27 @@ estimate_weights <- function(data,
 
   # bootstrapping
   outboot <- if (is.null(n_boot_iteration)) {
+    boot_seed <- NULL
+    boot_strata <- NULL
     NULL
   } else {
     # Make sure to leave '.Random.seed' as-is on exit
-    genv <- globalenv()
-    old_seed <- genv$.Random.seed
-    on.exit(suspendInterrupts({
-      if (is.null(old_seed)) {
-        rm(".Random.seed", envir = genv, inherits = FALSE)
-      } else {
-        assign(".Random.seed", value = old_seed, envir = genv, inherits = FALSE)
-      }
-    }))
+    old_seed <- globalenv()$.Random.seed
+    on.exit(suspendInterrupts(set_random_seed(old_seed)))
     set.seed(set_seed_boot)
+
     rowid_in_data <- which(!ind)
-    vapply(
-      seq_len(n_boot_iteration),
-      FUN.VALUE = matrix(0, nrow = nrow(EM), ncol = 2),
-      FUN = function(i) {
-        boot_rows <- sample(x = nrow(EM), size = nrow(EM), replace = TRUE)
-        boot_EM <- EM[boot_rows, , drop = FALSE]
-        boot_opt <- optimise_weights(matrix = boot_EM, par = alpha, method = method, ...)
-        cbind("rowid" = rowid_in_data[boot_rows], "weight" = boot_opt$wt[, 1])
-      }
-    )
+    arms <- factor(data$ARM[rowid_in_data])
+    boot_statistic <- function(d, w) optimise_weights(d[w, ], par = alpha, method = method, ...)$wt[, 1]
+    boot_out <- boot(EM, statistic = boot_statistic, R = n_boot_iteration, strata = arms)
+
+    boot_array <- array(dim = list(nrow(EM), 2, n_boot_iteration))
+    dimnames(boot_array) <- list(sampled_patient = NULL, c("rowid", "weight"), bootstrap_iteration = NULL)
+    boot_array[, 1, ] <- t(boot.array(boot_out, TRUE))
+    boot_array[, 2, ] <- t(boot_out$t)
+    boot_seed <- boot_out$seed
+    boot_strata <- boot_out$strata
+    boot_array
   }
 
   # append weights to data
@@ -147,7 +145,10 @@ estimate_weights <- function(data,
     nr_missing = nr_missing,
     ess = sum(wt)^2 / sum(wt^2),
     opt = opt1$opt,
-    boot = outboot
+    boot = outboot,
+    boot_seed = boot_seed,
+    boot_strata = boot_strata,
+    rows_with_missing = rows_with_missing
   )
 
   class(outdata) <- c("maicplus_estimate_weights", "list")
diff --git a/R/utils.R b/R/utils.R
new file mode 100644
index 00000000..3c05aea7
--- /dev/null
+++ b/R/utils.R
@@ -0,0 +1,21 @@
+# Restore the RNG back to a previous state using the global .Random.seed
+set_random_seed <- function(old_seed) {
+  if (is.null(old_seed)) {
+    rm(".Random.seed", envir = globalenv(), inherits = FALSE)
+  } else {
+    assign(".Random.seed", value = old_seed, envir = globalenv(), inherits = FALSE)
+  }
+}
+
+construct_boot_data <- function(weighted_data, i = 1) {
+  if (is.null(weighted_data$boot)) stop("Must contain bootstrap results from estimate_weights()")
+  i <- as.integer(i)
+  R <- dim(weighted_data$boot)[3]
+  if (i < 1 || i > R) stop("i must be integer between 1 and ", R)
+
+  boot_data <- weighted_data$boot[, , i]
+  weighted_data$data <- weighted_data$data[boot_data[, 1], ]
+  weighted_data$data$weights <- boot_data[, 2]
+  weighted_data$data$scaled_weights <- boot_data[, 2] / sum(boot_data[, 2])
+  weighted_data
+}
diff --git a/inst/examples/maic_anchored_ex.R b/inst/examples/maic_anchored_ex.R
index 489298ea..2cabe7e1 100644
--- a/inst/examples/maic_anchored_ex.R
+++ b/inst/examples/maic_anchored_ex.R
@@ -10,6 +10,7 @@ adsl <- read.csv(system.file("extdata", "adsl.csv",
 adsl$USUBJID <- paste0("xx", adsl$USUBJID)
 adsl2 <- adsl
 adsl2$USUBJID <- sample(size = nrow(adsl2), paste0("yy", adsl2$USUBJID), replace = FALSE)
+adsl2$ARM <- "C"
 adsl2 <- adsl2[order(adsl2$USUBJID), ]
 adsl <- rbind(adsl, adsl2)
 
@@ -29,6 +30,7 @@ tmp <- simulate(fit_C, nsim = 1, seed = 1234, newdata = adtte2, censtime = max(a
 adtte2$TIME <- tmp$time_1
 adtte2$EVENT <- tmp$event_1
 adtte2$USUBJID <- paste0("yy", adtte2$USUBJID)
+adtte2 <- adtte2[order(adtte2$USUBJID), ]
 adtte <- rbind(adtte, adtte2)
 
 ### AgD
diff --git a/inst/examples/maic_unanchored_binary_ex.R b/inst/examples/maic_unanchored_binary_ex.R
index e44b25f6..f6ff1646 100644
--- a/inst/examples/maic_unanchored_binary_ex.R
+++ b/inst/examples/maic_unanchored_binary_ex.R
@@ -8,6 +8,7 @@ weighted_data2 <- estimate_weights(data = centered_ipd_sat, centered_colnames =
 # binary IPD
 adrs_sat
 
+
 # get dummy binary IPD
 pseudo_adrs <- get_pseudo_ipd_binary(
   binary_agd = data.frame(
diff --git a/man/maic_anchored.Rd b/man/maic_anchored.Rd
index abe14f69..d616248f 100644
--- a/man/maic_anchored.Rd
+++ b/man/maic_anchored.Rd
@@ -17,7 +17,8 @@ maic_anchored(
   endpoint_name = "Time to Event Endpoint",
   eff_measure = c("HR", "OR", "RR", "RD", "Diff"),
   time_scale = "months",
-  km_conf_type = "log-log"
+  km_conf_type = "log-log",
+  boot_ci_type = c("norm", "basic", "stud", "perc", "bca")
 )
 }
 \arguments{
@@ -49,6 +50,9 @@ from contingency table (for binary endpoint)}
 'days'. NOTE: it is assumed that values in TIME column of \code{ipd} and \code{pseudo_ipd} is in the unit of days}
 
 \item{km_conf_type}{a string, pass to \code{conf.type} of \code{survfit}}
+
+\item{boot_ci_type}{a string, one of \code{c("norm","basic", "stud", "perc", "bca")} to select the type of bootstrap
+confidence interval. See \link[boot:boot.ci]{boot::boot.ci} for more details.}
 }
 \value{
 A list, contains 'descriptive' and 'inferential'
diff --git a/man/maic_unanchored.Rd b/man/maic_unanchored.Rd
index fbce19ae..0cc6fdb8 100644
--- a/man/maic_unanchored.Rd
+++ b/man/maic_unanchored.Rd
@@ -15,7 +15,7 @@ maic_unanchored(
   endpoint_type = "tte",
   endpoint_name = "Time to Event Endpoint",
   eff_measure = c("HR", "OR", "RR", "RD"),
-  boot_ci_is_quantile = FALSE,
+  boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
   time_scale = "months",
   km_conf_type = "log-log",
   binary_robust_cov_type = "HC3"
@@ -44,18 +44,16 @@ from contingency table (for binary endpoint)}
 \item{eff_measure}{a string, "RD" (risk difference), "OR" (odds ratio), "RR" (relative risk) for a binary endpoint;
 "HR" for a time-to-event endpoint. By default is \code{NULL}, "OR" is used for binary case, otherwise "HR" is used.}
 
-\item{boot_ci_is_quantile}{a logical, specify if the 95\% bootstrapped confidence interval should be derived by sample
-quantile. Default FALSE, which the estimates assumes to follow asymptotic normal (only if \code{eff_measure} is "RD") or
-log-normal with a variance that can be approximated by bootstrapped sample of the estimate. This default option may
-be handy when the number of bootstrap iterations is not big.}
+\item{boot_ci_type}{a string, one of \code{c("norm","basic", "stud", "perc", "bca")} to select the type of bootstrap
+confidence interval. See \link[boot:boot.ci]{boot::boot.ci} for more details.}
 
 \item{time_scale}{a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or
 'days'. NOTE: it is assumed that values in TIME column of \code{ipd} and \code{pseudo_ipd} is in the unit of days}
 
 \item{km_conf_type}{a string, pass to \code{conf.type} of \code{survfit}}
 
-\item{binary_robust_cov_type}{a string to pass to argument \code{type} of \link[sandwich:vcovHC]{sandwich::vcovHC}, see possible options
-in the documentation of that function. Default is \code{"HC3"}}
+\item{binary_robust_cov_type}{a string to pass to argument \code{type} of \link[sandwich:vcovHC]{sandwich::vcovHC}, see possible options in the
+documentation of that function. Default is \code{"HC3"}}
 }
 \value{
 A list, contains 'descriptive' and 'inferential'
diff --git a/tests/testthat/data/test_binary_unanchored_expected.RData b/tests/testthat/data/test_binary_unanchored_expected.RData
index cabeb380..5412e747 100644
Binary files a/tests/testthat/data/test_binary_unanchored_expected.RData and b/tests/testthat/data/test_binary_unanchored_expected.RData differ
diff --git a/tests/testthat/data/test_tte_unanchored_expected.RData b/tests/testthat/data/test_tte_unanchored_expected.RData
index aebf1a65..662e39e4 100644
Binary files a/tests/testthat/data/test_tte_unanchored_expected.RData and b/tests/testthat/data/test_tte_unanchored_expected.RData differ
diff --git a/tests/testthat/test-maic_anchored.R b/tests/testthat/test-maic_anchored.R
index 1cfbfb1c..7aa2848e 100644
--- a/tests/testthat/test-maic_anchored.R
+++ b/tests/testthat/test-maic_anchored.R
@@ -1,4 +1,4 @@
-test_that("maic_unanchored works for TTE using robust SE", {
+test_that("maic_anchored works for TTE using robust SE", {
   library(flexsurv)
   ### IPD
   set.seed(1234)
@@ -97,22 +97,16 @@ test_that("maic_unanchored works for TTE using robust SE", {
   )
   expect_equal(
     result$inferential$report_overall_robustCI$`median[95% CI]`,
-    c(
-      "7.6[6.3;10.3]", "1.8[1.6; 2.0]", "12.2[10.2; NA]", " 1.8[ 1.5;2.4]",
-      "2.7[2.3;3.3]", "1.9[1.7;2.0]", "-"
-    )
+    c("7.6[6.3;10.3]", "1.8[1.6; 2.0]", "12.2[10.2; NA]", " 1.8[ 1.5;2.4]", "2.7[2.3;3.3]", "1.9[1.7;2.0]", "-")
   )
   expect_equal(
     result$inferential$report_overall_robustCI$`HR[95% CI]`,
-    c(
-      "0.22[0.19;0.26]", "", "0.16[0.11;0.24]", "", "0.57[0.48;0.68]",
-      "", "0.29 [0.19; 0.44]"
-    )
+    c("0.22[0.19;0.26]", "", "0.16[0.11;0.24]", "", "0.57[0.48;0.68]", "", "0.29 [0.19; 0.44]")
   )
 })
 
 
-test_that("maic_unanchored works for TTE using bootstrap SE", {
+test_that("maic_anchored works for TTE using bootstrap SE", {
   # anchored example using maic_anchored for tte
   library(flexsurv)
   ### IPD
@@ -197,22 +191,24 @@ test_that("maic_unanchored works for TTE using bootstrap SE", {
 
   expect_equal(
     result$inferential$report_overall_bootCI$`median[95% CI]`,
-    c(
-      "7.6[6.3;10.3]", "1.8[1.6; 2.0]", "12.2[10.2; NA]", " 1.8[ 1.5;2.4]",
-      "2.7[2.3;3.3]", "1.9[1.7;2.0]", "-"
-    )
+    c("7.6[6.3;10.3]", "1.8[1.6; 2.0]", "12.2[10.2; NA]", " 1.8[ 1.5;2.4]", "2.7[2.3;3.3]", "1.9[1.7;2.0]", "-")
   )
   expect_equal(
     result$inferential$report_overall_bootCI$`HR[95% CI]`,
-    c(
-      "0.22[0.19;0.26]", "", "0.16[0.11;0.24]", "", "0.57[0.48;0.68]",
-      "", "0.29 [0.22; 0.38]"
-    )
+    c("0.22[0.19;0.26]", "", "0.16[0.11;0.24]", "", "0.57[0.48;0.68]", "", "0.29 [0.26; 0.44]")
   )
 
-  expect_length(result$inferential$boot_est, 5)
-  expect_equal(
-    quantile(result$inferential$boot_est, p = c(0.025, 0.5, 0.975)),
-    c(`2.5%` = 0.224505423383486, `50%` = 0.255147246438007, `97.5%` = 0.313359331451625)
+  t_matrix_expected <- matrix(
+    c(
+      c(-1.24294307251997, -1.33792890159168, -1.57070441400606, -1.52456404275395, -1.40942511608976),
+      c(0.0471386881163452, 0.0413517501082777, 0.0410548744406873, 0.0390747454633873, 0.04529038893612),
+      c(0.217114458561251, 0.203351297286931, 0.202620024777136, 0.197673330177309, 0.212815386981581),
+      c(-1.80190829720242, -1.89689412627413, -2.12966963868851, -2.0835292674364, -1.96839034077221),
+      c(0.197156067436005, 0.181888913677451, 0.181070984012272, 0.175518011252044, 0.192411579034645),
+      c(0.0388705149268306, 0.0330835769187631, 0.0327867012511726, 0.0308065722738727, 0.0370222157466054)
+    ),
+    byrow = FALSE,
+    ncol = 6
   )
+  expect_equal(result$inferential$boot_est$t, t_matrix_expected)
 })
diff --git a/tests/testthat/test-maic_unanchored.R b/tests/testthat/test-maic_unanchored.R
index c628e492..470b3b0f 100644
--- a/tests/testthat/test-maic_unanchored.R
+++ b/tests/testthat/test-maic_unanchored.R
@@ -9,7 +9,10 @@ test_that("test binary case", {
   centered_colnames <- paste0(centered_colnames, "_CENTERED")
 
   weighted_data <- estimate_weights(data = ipd_centered, centered_colnames = centered_colnames)
-  weighted_data2 <- estimate_weights(data = ipd_centered, centered_colnames = centered_colnames, n_boot_iteration = 20)
+  weighted_data2 <- estimate_weights(
+    data = ipd_centered, centered_colnames = centered_colnames, n_boot_iteration = 20,
+    set_seed_boot = 1234
+  )
 
   # get dummy binary IPD
   adrs <- read.csv(system.file("extdata", "adrs.csv", package = "maicplus", mustWork = TRUE))
@@ -66,10 +69,18 @@ test_that("test binary case", {
   }
 
   load(test_path("data", "test_binary_unanchored_expected.RData"))
+
+  # Compare robust outputs
   expect_equal(testout$descriptive, expectout$descriptive)
   expect_equal(testout$inferential, expectout$inferential)
+  expect_equal(testout$inferential$model_before, expectout$inferential$model_before)
+  expect_equal(testout$inferential$model_after, expectout$inferential$model_after)
+
+  # Compare bootstrap outputs
   expect_equal(testout2$descriptive, expectout2$descriptive)
-  expect_equal(testout2$inferential, expectout2$inferential)
+  expect_equal(testout2$inferential$boot_est["t"], expectout2$inferential$boot_est["t"])
+  expect_equal(testout2$inferential$boot_est["seed"], expectout2$inferential$boot_est["seed"])
+  expect_equal(testout2$inferential$report_overall_bootCI, expectout2$inferential$report_overall_bootCI)
 })
 
 
@@ -164,8 +175,15 @@ test_that("test time to event case", {
   }
 
   load(test_path("data", "test_tte_unanchored_expected.RData"))
+  # Compare robust outputs
   expect_equal(testout$descriptive, expectout$descriptive)
   expect_equal(testout$inferential, expectout$inferential)
+  expect_equal(testout$inferential$model_before, expectout$inferential$model_before)
+  expect_equal(testout$inferential$model_after, expectout$inferential$model_after)
+
+  # Compare bootstrap outputs
   expect_equal(testout2$descriptive, expectout2$descriptive)
-  expect_equal(testout2$inferential, expectout2$inferential)
+  expect_equal(testout2$inferential$boot_est["t"], expectout2$inferential$boot_est["t"])
+  expect_equal(testout2$inferential$boot_est["seed"], expectout2$inferential$boot_est["seed"])
+  expect_equal(testout2$inferential$report_overall_bootCI, expectout2$inferential$report_overall_bootCI)
 })
diff --git a/tests/testthat/test-matching.R b/tests/testthat/test-matching.R
index 75cb8086..b87c387d 100644
--- a/tests/testthat/test-matching.R
+++ b/tests/testthat/test-matching.R
@@ -86,11 +86,11 @@ test_that("estimate_weights works as expected with bootstrapping", {
 
   expected_matrix <- structure(
     c(
-      c(411, 324, 61, 71, 361),
-      c(0.0001657791105167, 0.382238891368213, 1.41902681600543e-10, 5.04595364860806e-08, 0.434732317134287)
+      c(411, 71, 321, 150, 440),
+      c(8.71745219703408e-05, 1.93529165335151e-08, 0.886392116942025, 1.42604077815808e-13, 0.802986096194834)
     ),
     dim = c(5L, 2L),
-    dimnames = list(c("411", "324", "61", "71", "361"), c("rowid", "weight"))
+    dimnames = list("sampled_patient" = NULL, c("rowid", "weight"))
   )
   expect_equal(result$boot[1:5, , 1], expected_matrix)
 })
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
new file mode 100644
index 00000000..73cb1cb2
--- /dev/null
+++ b/tests/testthat/test-utils.R
@@ -0,0 +1,12 @@
+test_that("set_random_seed works", {
+  original_seed <- globalenv()$.Random.seed
+  set.seed(123)
+  seed_123 <- globalenv()$.Random.seed
+  sample(10)
+  # Back to a user specified state
+  set_random_seed(seed_123)
+  expect_equal(seed_123, globalenv()$.Random.seed)
+  # Back to the original state
+  set_random_seed(original_seed)
+  expect_equal(original_seed, globalenv()$.Random.seed)
+})