diff --git a/.gitignore b/.gitignore
index ba2ec833b..9e1aaeed3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,4 +15,4 @@ inst/shiny/log/*
 .DS_Store
 # {shinytest2}: Ignore new debug snapshots for `$expect_values()`
 *_.new.png
-desktop.ini
\ No newline at end of file
+desktop.ini
diff --git a/DESCRIPTION b/DESCRIPTION
index eb8388c36..a457258b1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: aNCA
 Title: (Pre-)Clinical NCA in a Dynamic Shiny App
-Version: 0.1.0.9184
+Version: 0.1.0.9187
 Authors@R: c(
     person("Ercan", "Suekuer", email = "ercan.suekuer@roche.com", role = "aut",
            comment = c(ORCID = "0009-0001-1626-1526")),
diff --git a/NEWS.md b/NEWS.md
index 49d3d4af1..bf1f273ed 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,6 +25,9 @@
 * Partial interval parameters section supports calculations beyond `AUCINT`: `RCAMINT`, `AUCINTD`, `CAVGINT`, and others. Table starts empty by default with a Remove Row button (#524, #1249)
 * "Min. Points for Half-life" setting added (range 2–10, default 3) (#1155)
 * BLQ imputation rules via `NCA Setup > Data Imputation` (#139)
+
+### Bug Fixes
+* Half-life (LAMZHL) and lambda.z (LAMZ) now correctly retain BLQ imputation when half-life-dependent parameters (AUCIFO, AUCIFP) are requested. Previously `rm_impute_obs_params()` removed imputation from `half.life` because its dependency check was limited to one level — missing the transitive chain `half.life -> lambda.z -> aucinf.obs`. The dependency resolution now recursively traverses upstream dependencies to include all parameters in the AUC calculation chain, and also walks downstream so AUC consumers (e.g., `vss.obs`, `vz.obs`) keep the same imputed data (#1057).
 * General Exclusions section for in-app NCA exclusions, with "Excl. TLG" checkbox per entry (#851, #1018)
 * Parameter Exclusions tab: exclude individual PK parameter rows from descriptive statistics and ADPP export via PPSUMFL/PPSUMRSN flags (#1040)
 * NCA flag rules (NCAwXRS) from ADNCA standards — flagged records are excluded from NCA (#752)
diff --git a/R/intervals.R b/R/intervals.R
index 0db736fa0..20422f1ac 100644
--- a/R/intervals.R
+++ b/R/intervals.R
@@ -295,6 +295,12 @@ update_main_intervals <- function(
   # and apply it only for non-observational parameters
 
   if (!is.null(blq_imputation_rule)) {
+    # Ensure impute column exists so dplyr mutate below references the
+    # column rather than the function parameter (which could be FALSE
+    # from YAML settings, causing "PKNCA_impute_method_FALSE" error).
+    if (!"impute" %in% names(data$intervals)) {
+      data$intervals$impute <- NA_character_
+    }
     data$intervals <- data$intervals %>%
       mutate(
         impute = ifelse(
@@ -324,16 +330,26 @@ update_main_intervals <- function(
 #' @import dplyr
 #'
 rm_impute_obs_params <- function(data, metadata_nca_parameters = metadata_nca_parameters) {
-  # Don't impute parameters that are not AUC dependent
+  # Don't impute parameters that are not AUC dependent.
+  # Parameters in the AUC calculation chain (half.life feeds lambda.z,
+  # which feeds aucinf.obs) must all use the same BLQ-imputed data (#1057).
   params_auc_dep <- metadata_nca_parameters %>%
     filter(grepl("auc|aumc", PKNCA) | grepl("auc", Depends)) %>%
     pull(PKNCA)
 
+  # Build consumer map ("who consumes X") and walk upstream from AUC params
+  # to find all parameters in their dependency chain (#1057).
+  consumer_map <- .build_consumer_map(metadata_nca_parameters)
+  needs_impute <- .find_upstream_deps(params_auc_dep, consumer_map)
+
+  # Walk downstream too: AUC consumers (e.g., vss.obs = f(cl.obs, mrt.obs),
+  # vz.obs = f(cl.obs, lambda.z)) compute AUC-derived quantities internally,
+  # so they must use the same imputed data for consistency (#1057).
+  dependency_map <- .build_dependency_map(metadata_nca_parameters)
+  needs_impute <- .find_downstream_consumers(needs_impute, dependency_map)
+
   params_not_to_impute <- metadata_nca_parameters %>%
-    filter(
-      !grepl("auc|aumc", PKNCA),
-      !grepl(paste0(params_auc_dep, collapse = "|"), Depends)
-    ) %>%
+    filter(!PKNCA %in% needs_impute) %>%
     pull(PKNCA) %>%
     intersect(names(PKNCA::get.interval.cols()))
 
@@ -367,3 +383,95 @@ rm_impute_obs_params <- function(data, metadata_nca_parameters = metadata_nca_pa
 
   data
 }
+
+#' Build a consumer map from the Depends column.
+#' For each parameter A, returns which parameters consume A (list A as a dependency).
+#' e.g., lambda.z lists half.life in Depends, so consumer_map$half.life = "lambda.z".
+#' @noRd
+.build_consumer_map <- function(metadata) {
+  rev <- list()
+  for (i in seq_len(nrow(metadata))) {
+    pkg_name <- metadata$PKNCA[i]
+    dep_str <- metadata$Depends[i]
+    if (is.na(dep_str) || dep_str == "") next
+    dep_list <- trimws(strsplit(dep_str, ",")[[1]])
+    for (d in dep_list) {
+      rev[[d]] <- unique(c(rev[[d]], pkg_name))
+    }
+  }
+  rev
+}
+
+#' Build a dependency map from the Depends column.
+#' Mirror image of `.build_consumer_map()`: for each parameter A, returns the
+#' parameters A depends on.
+#' e.g., vss.obs lists "cl.obs, mrt.obs" in Depends, so
+#' dependency_map$vss.obs = c("cl.obs", "mrt.obs").
+#' @noRd
+.build_dependency_map <- function(metadata) {
+  fwd <- list()
+  for (i in seq_len(nrow(metadata))) {
+    pkg_name <- metadata$PKNCA[i]
+    dep_str <- metadata$Depends[i]
+    if (is.na(dep_str) || dep_str == "") next
+    fwd[[pkg_name]] <- trimws(strsplit(dep_str, ",")[[1]])
+  }
+  fwd
+}
+
+#' Check if a parameter is a new upstream consumer of the current chain.
+#' @noRd
+.is_new_upstream_consumer <- function(pkg, consumer_map, needs, obs_params) {
+  !pkg %in% needs && !pkg %in% obs_params && any(consumer_map[[pkg]] %in% needs)
+}
+
+#' Find all upstream dependencies transitively from `start_set`.
+#' Walks the consumer map to collect params that feed into the current chain,
+#' stopping at purely observational leaf params (cmax, tmax, tlast).
+#'
+#' @param start_set Character vector of starting PKNCA parameter names.
+#' @param consumer_map Named list from `.build_consumer_map()`.
+#' @param obs_params Character vector of observational params to exclude.
+#'   Must be kept in sync with metadata_nca_parameters when new leaf params
+#'   are added. Default: cmax, tmax, tlast.
+#' @param max_iter Maximum iterations to guard against infinite loops from
+#'   circular dependencies. 50 is generous (~40 params in real metadata).
+#' @noRd
+.find_upstream_deps <- function(start_set, consumer_map,
+                                obs_params = c("cmax", "tmax", "tlast"),
+                                max_iter = 50L) {
+  needs <- start_set
+  for (iter in seq_len(max_iter)) {
+    newly_found <- character()
+    for (pkg in names(consumer_map)) {
+      if (.is_new_upstream_consumer(pkg, consumer_map, needs, obs_params)) {
+        newly_found <- c(newly_found, pkg)
+      }
+    }
+    if (length(newly_found) == 0) break
+    needs <- c(needs, newly_found)
+  }
+  needs
+}
+
+#' Find all downstream consumers transitively from `start_set`.
+#' Params whose Depends list intersects the current chain consume AUC-derived
+#' quantities (e.g., vss.obs consumes cl.obs and mrt.obs) and therefore compute
+#' AUC internally — they must use the same BLQ-imputed data (#1057).
+#'
+#' The fixpoint walk is direction-agnostic: `.find_upstream_deps()` follows
+#' "who consumes X" edges, so passing the reverse (dependency) map from
+#' `.build_dependency_map()` walks the graph downstream. Purely observational
+#' leaf params (cmax, tmax, tlast) are excluded as in the upstream walk.
+#'
+#' @param start_set Character vector of starting PKNCA parameter names.
+#' @param dependency_map Named list from `.build_dependency_map()`.
+#' @param obs_params Character vector of observational params to exclude.
+#' @param max_iter Maximum iterations to guard against infinite loops from
+#'   circular dependencies.
+#' @noRd
+.find_downstream_consumers <- function(start_set, dependency_map,
+                                       obs_params = c("cmax", "tmax", "tlast"),
+                                       max_iter = 50L) {
+  .find_upstream_deps(start_set, dependency_map, obs_params, max_iter)
+}
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 4d990a028..deb0c4880 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -7,7 +7,9 @@ AEFRLT
 AFRLT
 ARRLT
 ATPTREF
+AUCIFO
 AUCIFOD
+AUCIFP
 AUCINT
 AUCLSTD
 AUCPEO
@@ -58,6 +60,7 @@ INTRAVASCULAR
 Kezia
 Kobana
 LAMZ
+LAMZHL
 LAMZLL
 LAMZNPT
 LAMZSPN
diff --git a/tests/testthat/data/test-blq-ADNCA.csv b/tests/testthat/data/test-blq-ADNCA.csv
new file mode 100644
index 000000000..f49390beb
--- /dev/null
+++ b/tests/testthat/data/test-blq-ADNCA.csv
@@ -0,0 +1,13 @@
+"STUDYID","USUBJID","PCSPEC","PARAM","DOSETRT","DOSEA","DOSEU","ROUTE","ADOSEDUR","AVAL","AVALU","AFRLT","NFRLT","ARRLT","NRRLT","RRLTU","ATPTREF","TRT01A","METABFL"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,100,"ng/mL",0.5,0.5,0.5,0.5,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,80,"ng/mL",1,1,1,1,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,65,"ng/mL",1.5,1.5,1.5,1.5,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,52,"ng/mL",2,2,2,2,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,35,"ng/mL",3,3,3,3,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,25,"ng/mL",4,4,4,4,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,12,"ng/mL",6,6,6,6,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,6,"ng/mL",8,8,8,8,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,2.5,"ng/mL",12,12,12,12,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,0,"ng/mL",24,24,24,24,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,0,"ng/mL",36,36,36,36,"hr","DOSE 1","DrugA 10 mg","N"
+"S1","BLQ-TEST-01","SERUM","DrugA","DrugA",10,"mg","INTRAVENOUS BOLUS",0,0,"ng/mL",48,48,48,48,"hr","DOSE 1","DrugA 10 mg","N"
diff --git a/tests/testthat/test-intervals.R b/tests/testthat/test-intervals.R
index 82a73b7e0..0d44c63f9 100644
--- a/tests/testthat/test-intervals.R
+++ b/tests/testthat/test-intervals.R
@@ -449,3 +449,254 @@ describe("rm_impute_obs_params", {
     expect_equal(result$intervals, intervals_before)
   })
 })
+
+describe(".build_consumer_map", {
+  it("builds consumer map from Depends column", {
+    metadata <- data.frame(
+      PKNCA = c("half.life", "lambda.z", "aucinf.obs", "cmax"),
+      Depends = c("tmax, tlast", "half.life", "lambda.z", NA),
+      stringsAsFactors = FALSE
+    )
+    result <- .build_consumer_map(metadata)
+    expect_type(result, "list")
+    expect_true("lambda.z" %in% result[["half.life"]])
+    expect_true("aucinf.obs" %in% result[["lambda.z"]])
+    expect_null(result[["cmax"]])
+  })
+
+  it("handles empty Depends strings", {
+    metadata <- data.frame(
+      PKNCA = c("param1", "param2"),
+      Depends = c("", NA),
+      stringsAsFactors = FALSE
+    )
+    result <- .build_consumer_map(metadata)
+    expect_type(result, "list")
+  })
+})
+
+describe(".find_upstream_deps", {
+  it("finds half.life via transitive chain: half.life -> lambda.z -> aucinf.obs", {
+    metadata <- data.frame(
+      PKNCA = c("half.life", "lambda.z", "aucinf.obs", "cmax", "tmax"),
+      Depends = c("tmax, tlast", "half.life", "lambda.z", NA, NA),
+      stringsAsFactors = FALSE
+    )
+    consumer_map <- .build_consumer_map(metadata)
+    start_set <- c("aucinf.obs")
+    result <- .find_upstream_deps(start_set, consumer_map)
+    expect_true("aucinf.obs" %in% result)
+    expect_true("lambda.z" %in% result)
+    expect_true("half.life" %in% result)
+    expect_false("cmax" %in% result)
+    expect_false("tmax" %in% result)
+  })
+
+  it("excludes observational leaf params (cmax, tmax, tlast) regardless of depth", {
+    metadata <- data.frame(
+      PKNCA = c("half.life", "lambda.z", "aucinf.obs"),
+      Depends = c("tmax", "half.life", "lambda.z"),
+      stringsAsFactors = FALSE
+    )
+    consumer_map <- .build_consumer_map(metadata)
+    result <- .find_upstream_deps(c("aucinf.obs"), consumer_map)
+    expect_true("lambda.z" %in% result)
+    expect_true("half.life" %in% result)
+    # tmax is in default obs_params → excluded regardless of depth
+    expect_false("tmax" %in% result)
+  })
+})
+
+describe(".find_downstream_consumers", {
+  it("finds AUC consumers like vss.obs/vz.obs via the dependency map (#1057)", {
+    metadata <- data.frame(
+      PKNCA = c("aucinf.obs", "cl.obs", "mrt.obs", "vss.obs", "vz.obs",
+                "cmax", "cmax.dn"),
+      Depends = c("auclast, clast.obs, lambda.z", "aucinf.obs",
+                  "aucinf.obs, aumcinf.obs", "cl.obs, mrt.obs",
+                  "cl.obs, lambda.z", NA, "cmax"),
+      stringsAsFactors = FALSE
+    )
+    dependency_map <- .build_dependency_map(metadata)
+    start_set <- c("aucinf.obs", "cl.obs", "mrt.obs", "lambda.z")
+    result <- .find_downstream_consumers(start_set, dependency_map)
+    expect_true("vss.obs" %in% result)
+    expect_true("vz.obs" %in% result)
+    # cmax.dn only depends on cmax, which is not in the chain → not added
+    expect_false("cmax" %in% result)
+    expect_false("cmax.dn" %in% result)
+  })
+
+  it("excludes observational leaf params even if they depend on the chain", {
+    metadata <- data.frame(
+      PKNCA = c("auclast", "cmax"),
+      Depends = c(NA, "auclast"),
+      stringsAsFactors = FALSE
+    )
+    dependency_map <- .build_dependency_map(metadata)
+    result <- .find_downstream_consumers(c("auclast"), dependency_map)
+    expect_false("cmax" %in% result)
+  })
+
+  it("walks transitive downstream chains", {
+    metadata <- data.frame(
+      PKNCA = c("cl.obs", "vss.obs", "vss.dn"),
+      Depends = c("aucinf.obs", "cl.obs", "vss.obs"),
+      stringsAsFactors = FALSE
+    )
+    dependency_map <- .build_dependency_map(metadata)
+    result <- .find_downstream_consumers(c("cl.obs"), dependency_map)
+    expect_true("vss.obs" %in% result)
+    expect_true("vss.dn" %in% result)
+  })
+})
+
+describe("rm_impute_obs_params integration", {
+  it("retains imputation for half-life chain and removes it for observational params (#1057)", {
+    data <- FIXTURE_PKNCA_DATA
+    # Mark all intervals as having imputation
+    if (!"impute" %in% names(data$intervals)) {
+      data$intervals$impute <- "blq"
+    } else {
+      data$intervals$impute[is.na(data$intervals$impute)] <- "blq"
+    }
+
+    # Request AUC-chain params and observational params
+    requested <- c("aucinf.obs", "half.life", "cmax", "tmax", "tlast")
+    for (col in names(data$intervals)) {
+      if (col %in% c("start", "end", "impute", "type_interval")) next
+      data$intervals[[col]] <- col %in% requested
+    }
+
+    result <- rm_impute_obs_params(data, metadata_nca_parameters)
+
+    # AUC-chain params (half.life, lambda.z, aucinf.obs) should RETAIN imputation
+    auc_chain_rows <- result$intervals[
+      result$intervals[["aucinf.obs"]] |
+        result$intervals[["half.life"]] |
+        result$intervals[["lambda.z"]],
+    ]
+    expect_true("blq" %in% auc_chain_rows$impute)
+
+    # Observational params (cmax, tmax, tlast) should LOSE imputation
+    obs_rows <- result$intervals[
+      result$intervals[["cmax"]] |
+        result$intervals[["tmax"]] |
+        result$intervals[["tlast"]],
+    ]
+    expect_false("blq" %in% obs_rows$impute)
+    expect_true(all(is.na(obs_rows$impute) | obs_rows$impute == ""))
+  })
+})
+
+describe("BLQ imputation end-to-end on real bug dataset (#1057)", {
+  # Dataset attached to issue #1057: IV bolus profile with trailing BLQ (0)
+  # concentrations at 24/36/48 hr. Before the fix, rm_impute_obs_params()
+  # removed BLQ imputation from half.life (dependency check was one level
+  # deep), so lambda.z was calculated on non-imputed data while aucinf.obs
+  # used imputed data, breaking the AUCIFO = AUCLST + CLST/LAMZ identity.
+  blq_adnca <- read.csv(testthat::test_path("data", "test-blq-ADNCA.csv"),
+                        na.strings = c("", "NA"))
+  blq_rule <- list(first = "keep", middle = "drop", last = 0)
+  auc_chain_params <- c("auclast", "aucinf.obs", "aucinf.pred",
+                        "half.life", "lambda.z", "clast.obs", "clast.pred",
+                        "cmax", "tmax", "tlast")
+
+  pknca_data <- PKNCA_create_data_object(blq_adnca)
+  study_types <- unique(.derive_study_types(pknca_data)$type)
+  parameter_selections <- setNames(
+    lapply(study_types, function(x) auc_chain_params),
+    study_types
+  )
+
+  updated_data <- PKNCA_update_data_object(
+    adnca_data = pknca_data,
+    method = "lin up/log down",
+    selected_analytes = "DrugA",
+    selected_profile = "DOSE 1",
+    selected_pcspec = "SERUM",
+    start_impute = FALSE,
+    parameter_selections = parameter_selections,
+    blq_imputation_rule = blq_rule
+  )
+  nca_results <- PKNCA_calculate_nca(updated_data, blq_rule = blq_rule)
+  results_df <- as.data.frame(nca_results$result)
+  get_pp <- function(pp) results_df$PPORRES[results_df$PPTESTCD == pp]
+
+  it("keeps BLQ imputation on the half-life chain and removes it from observational params", {
+    auc_chain_rows <- updated_data$intervals[updated_data$intervals$aucinf.obs, ]
+    expect_true("blq" %in% auc_chain_rows$impute)
+
+    obs_rows <- updated_data$intervals[updated_data$intervals$cmax, ]
+    expect_true(all(is.na(obs_rows$impute) | obs_rows$impute == ""))
+  })
+
+  it("produces internally consistent AUCIFO = AUCLST + CLST/LAMZ (obs and pred)", {
+    auclst <- get_pp("auclast")
+    lambda_z <- get_pp("lambda.z")
+
+    expect_false(any(is.na(c(auclst, lambda_z, get_pp("aucinf.obs")))))
+    expect_equal(get_pp("aucinf.obs"), auclst + get_pp("clast.obs") / lambda_z,
+                 tolerance = 1e-6)
+    expect_equal(get_pp("aucinf.pred"), auclst + get_pp("clast.pred") / lambda_z,
+                 tolerance = 1e-6)
+  })
+
+  it("matches the parameter values obtained in the app for the issue dataset", {
+    expect_equal(get_pp("auclast"), 251.4521, tolerance = 1e-4)
+    expect_equal(get_pp("aucinf.obs"), 259.2045, tolerance = 1e-4)
+    expect_equal(get_pp("lambda.z"), 0.32248, tolerance = 1e-4)
+    expect_equal(get_pp("half.life"), 2.1494, tolerance = 1e-4)
+  })
+
+  it("keeps imputation for downstream AUC consumers (vss.obs, vz.obs) and stays warning-free", {
+    # vss.obs/vz.obs have no "auc" in name or Depends, but compute AUC
+    # internally via cl.obs/mrt.obs/lambda.z. Stripping their imputation
+    # makes PKNCA warn "AUC range starting before the first measurement"
+    # and computes them on non-imputed data (#1057 downstream regression).
+    default_like_params <- c(
+      "aucinf.obs", "auclast", "cmax", "clast.obs", "tlast", "tmax",
+      "half.life", "cl.obs", "vss.obs", "vz.obs", "mrt.last", "mrt.obs",
+      "lambda.z", "r.squared", "span.ratio", "adj.r.squared"
+    )
+    default_selections <- setNames(
+      lapply(study_types, function(x) default_like_params),
+      study_types
+    )
+    default_data <- PKNCA_update_data_object(
+      adnca_data = PKNCA_create_data_object(blq_adnca),
+      method = "lin up/log down",
+      selected_analytes = "DrugA",
+      selected_profile = "DOSE 1",
+      selected_pcspec = "SERUM",
+      start_impute = TRUE,
+      parameter_selections = default_selections,
+      blq_imputation_rule = blq_rule
+    )
+
+    # Downstream consumers stay in the imputed row, cmax stays stripped
+    vss_rows <- default_data$intervals[default_data$intervals$vss.obs, ]
+    expect_true(all(grepl("blq", vss_rows$impute)))
+    cmax_rows <- default_data$intervals[default_data$intervals$cmax, ]
+    expect_true(all(is.na(cmax_rows$impute) | cmax_rows$impute == ""))
+
+    # No "AUC range starting before the first measurement" warning
+    run_warnings <- character()
+    default_results <- withCallingHandlers(
+      PKNCA_calculate_nca(default_data, blq_rule = blq_rule),
+      warning = function(w) {
+        run_warnings <<- c(run_warnings, conditionMessage(w))
+        invokeRestart("muffleWarning")
+      }
+    )
+    expect_false(any(grepl("before the first measurement", run_warnings)))
+
+    # Consumers are consistent with the imputed AUC chain values
+    default_df <- as.data.frame(default_results$result)
+    get_dp <- function(pp) default_df$PPORRES[default_df$PPTESTCD == pp]
+    expect_equal(get_dp("vz.obs"), get_dp("cl.obs") / get_dp("lambda.z"),
+                 tolerance = 1e-6)
+    expect_equal(get_dp("vss.obs"), get_dp("cl.obs") * get_dp("mrt.obs"),
+                 tolerance = 1e-6)
+  })
+})