diff --git a/tests/testthat/test-independent_test_early_zero_weight.R b/tests/testthat/test-independent_test_early_zero_weight.R
new file mode 100644
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+++ b/tests/testthat/test-independent_test_early_zero_weight.R
@@ -0,0 +1,170 @@
+test_that("early_zero_weight() with unstratified data", {
+  set.seed(123456)
+  early_period <- 2
+
+  output <- sim_pw_surv(n = 200) |>
+    cut_data_by_event(125) |>
+    counting_process(arm = "experimental") |>
+    early_zero_weight(early_period = early_period)
+
+  observed <- output$weight
+  expected <- dplyr::if_else(output$tte < early_period, 0, 1)
+  expect_equal(observed, expected)
+})
+
+test_that("early_zero_weight() with stratified data when fail_rate is not provided", {
+  set.seed(123456)
+  early_period <- 2
+  n <- 500
+
+  # Two strata
+  stratum <- c("Biomarker-positive", "Biomarker-negative")
+  prevalence_ratio <- c(0.6, 0.4)
+  enroll_rate <- gsDesign2::define_enroll_rate(
+    stratum = rep(stratum, each = 2),
+    duration = c(2, 10, 2, 10),
+    rate = c(c(1, 4) * prevalence_ratio[1], c(1, 4) * prevalence_ratio[2])
+  )
+  enroll_rate$rate <- enroll_rate$rate * n / sum(enroll_rate$duration * enroll_rate$rate)
+  # Failure rate
+  med_pos <- 10 # Median of the biomarker positive population
+  med_neg <- 8 # Median of the biomarker negative population
+  hr_pos <- c(1, 0.7) # Hazard ratio of the biomarker positive population
+  hr_neg <- c(1, 0.8) # Hazard ratio of the biomarker negative population
+  fail_rate <- gsDesign2::define_fail_rate(
+    stratum = rep(stratum, each = 2),
+    duration = c(3, 1000, 4, 1000),
+    fail_rate = c(log(2) / c(med_pos, med_pos, med_neg, med_neg)),
+    hr = c(hr_pos, hr_neg),
+    dropout_rate = 0.01
+  )
+  temp <- to_sim_pw_surv(fail_rate) # Convert the failure rate
+  x <- sim_pw_surv(
+    n = n, # Sample size
+    # Stratified design with prevalence ratio of 6:4
+    stratum = data.frame(stratum = stratum, p = prevalence_ratio),
+    # Randomization ratio
+    block = c("control", "control", "experimental", "experimental"),
+    enroll_rate = enroll_rate, # Enrollment rate
+    fail_rate = temp$fail_rate, # Failure rate
+    dropout_rate = temp$dropout_rate # Dropout rate
+  ) |>
+    cut_data_by_event(125) |>
+    counting_process(arm = "experimental")
+
+  expect_error(
+    early_zero_weight(x, early_period = early_period),
+    "For stratified design to use `early_zero_weight\\(\\)`, `fail_rate` can't be `NULL`."
+  )
+})
+
+test_that("early_zero_weight() with stratified data when fail_rate is not correctly provided", {
+  set.seed(123456)
+  early_period <- 2
+  n <- 500
+
+  # Two strata
+  stratum <- c("Biomarker-positive", "Biomarker-negative")
+  prevalence_ratio <- c(0.6, 0.4)
+  enroll_rate <- gsDesign2::define_enroll_rate(
+    stratum = rep(stratum, each = 2),
+    duration = c(2, 10, 2, 10),
+    rate = c(c(1, 4) * prevalence_ratio[1], c(1, 4) * prevalence_ratio[2])
+  )
+  enroll_rate$rate <- enroll_rate$rate * n / sum(enroll_rate$duration * enroll_rate$rate)
+  # Failure rate
+  med_pos <- 10 # Median of the biomarker positive population
+  med_neg <- 8 # Median of the biomarker negative population
+  hr_pos <- c(1, 0.7) # Hazard ratio of the biomarker positive population
+  hr_neg <- c(1, 0.8) # Hazard ratio of the biomarker negative population
+  fail_rate <- gsDesign2::define_fail_rate(
+    stratum = rep(stratum, each = 2),
+    duration = c(3, 1000, 4, 1000),
+    fail_rate = c(log(2) / c(med_pos, med_pos, med_neg, med_neg)),
+    hr = c(hr_pos, hr_neg),
+    dropout_rate = 0.01
+  )
+  fail_rate_wrong <- data.frame(
+    stratum = c(rep("Biomarker-positive", 3), rep("Biomarker-negative", 2)),
+    duration = c(3, 4, 1000, 4, 1000),
+    fil_rate = c(log(2) / c(med_pos, med_pos, med_pos, med_neg, med_neg)),
+    dropout_rate = rep(0.01, 5),
+    hr = c(1, 1, 0.7, 1, 0.8),
+    stringsAsFactors = FALSE
+  )
+  temp <- to_sim_pw_surv(fail_rate) # Convert the failure rate
+  x <- sim_pw_surv(
+    n = n, # Sample size
+    # Stratified design with prevalence ratio of 6:4
+    stratum = data.frame(stratum = stratum, p = prevalence_ratio),
+    # Randomization ratio
+    block = c("control", "control", "experimental", "experimental"),
+    enroll_rate = enroll_rate, # Enrollment rate
+    fail_rate = temp$fail_rate, # Failure rate
+    dropout_rate = temp$dropout_rate # Dropout rate
+  ) |>
+    cut_data_by_event(125) |>
+    counting_process(arm = "experimental")
+
+  expect_error(
+    early_zero_weight(x, early_period = early_period, fail_rate = fail_rate_wrong),
+    "`early_zero_weight\\(\\)` only allows delayed treatment effect, that is, 2 piece failure rate with HR = 1 at the first period."
+  )
+})
+
+test_that("early_zero_weight() with stratified data when fail_rate is correctly provided", {
+  set.seed(123456)
+  early_period <- 2
+  n <- 500
+  # Two strata
+  stratum <- c("Biomarker-positive", "Biomarker-negative")
+  prevalence_ratio <- c(0.6, 0.4)
+  enroll_rate <- gsDesign2::define_enroll_rate(
+    stratum = rep(stratum, each = 2),
+    duration = c(2, 10, 2, 10),
+    rate = c(c(1, 4) * prevalence_ratio[1], c(1, 4) * prevalence_ratio[2])
+  )
+  enroll_rate$rate <- enroll_rate$rate * n / sum(enroll_rate$duration * enroll_rate$rate)
+  # Failure rate
+  med_pos <- 10 # Median of the biomarker positive population
+  med_neg <- 8 # Median of the biomarker negative population
+  hr_pos <- c(1, 0.7) # Hazard ratio of the biomarker positive population
+  hr_neg <- c(1, 0.8) # Hazard ratio of the biomarker negative population
+  fail_rate <- gsDesign2::define_fail_rate(
+    stratum = rep(stratum, each = 2),
+    duration = c(3, 1000, 4, 1000),
+    fail_rate = c(log(2) / c(med_pos, med_pos, med_neg, med_neg)),
+    hr = c(hr_pos, hr_neg),
+    dropout_rate = 0.01
+  )
+  fail_rate_wrong <- data.frame(
+    stratum = c(rep("Biomarker-positive", 3), rep("Biomarker-negative", 2)),
+    duration = c(3, 4, 1000, 4, 1000),
+    fil_rate = c(log(2) / c(med_pos, med_pos, med_pos, med_neg, med_neg)),
+    dropout_rate = rep(0.01, 5),
+    hr = c(1, 1, 0.7, 1, 0.8),
+    stringsAsFactors = FALSE
+  )
+  temp <- to_sim_pw_surv(fail_rate) # Convert the failure rate
+  output <- sim_pw_surv(
+    n = n, # Sample size
+    # Stratified design with prevalence ratio of 6:4
+    stratum = data.frame(stratum = stratum, p = prevalence_ratio),
+    # Randomization ratio
+    block = c("control", "control", "experimental", "experimental"),
+    enroll_rate = enroll_rate, # Enrollment rate
+    fail_rate = temp$fail_rate, # Failure rate
+    dropout_rate = temp$dropout_rate # Dropout rate
+  ) |>
+    cut_data_by_event(125) |>
+    counting_process(arm = "experimental") |>
+    early_zero_weight(early_period = early_period, fail_rate = fail_rate)
+
+  observed <- output$weight
+  expected <- dplyr::if_else(
+    output$stratum == "Biomarker-negative",
+    dplyr::if_else(output$tte < 4, 0, log(0.8)),
+    dplyr::if_else(output$tte < 3, 0, log(0.7))
+  )
+  expect_equal(observed, expected)
+})