TSFLAB03A

Subjects With =1 Laboratory Values With Elevated or Low Values Based on Worst On-treatment Value Using NCI-CTCAE Criteria by Subgroup

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Code

# Program Name:              tsflab03a

# Prep Environment

library(envsetup)
library(tern)
library(dplyr)
library(rtables)
library(junco)

# Define script level parameters:

tblid <- "TSFLAB03a"
fileid <- tblid
titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

popfl <- "SAFFL"
trtvar <- "TRT01A"
ctrl_grp <- "Placebo"

subgrpvar <- "AGEGR1"
subgrplbl <- "Age: %s years"

page_by <- TRUE # Set page_by TRUE/FALSE if you (do not) wish to start a new page after a new subgroup
indent_adj <- -1L
if (page_by) {
  indent_adj <- 0L
}

## if the option TRTEMFL needs to be added to the TLF
trtemfl <- FALSE

## For analysis on SI units: use adlb dataset
## For analysis on Conventional units: use adlbc dataset -- shell is in conventional units

ad_domain <- "ADLB"

# Initial processing of data + check if table is valid for trial:

adlb_complete <- pharmaverseadamjnj::adlb


## parcat5 and 6 options :

availparcat56 <- c(
  "Investigations",
  "Metabolism and nutritional disorders",
  "Renal and urinary disorders",
  "Blood and lymphatic system disorders"
)

## resrict to some
selparcat56 <- availparcat56[c(1, 2, 4)]

## get all
selparcat56 <- availparcat56

lbtoxgrade_file <- file.path('../../_data', "lbtoxgrade.xlsx")
lbtoxgrade_sheets <- readxl::excel_sheets(path = lbtoxgrade_file)

### CTC5 or DAIDS21c : default CTC5

lbtoxgrade_defs <- readxl::read_excel(lbtoxgrade_file, sheet = "CTC5")

lbtoxgrade_defs <- unique(
  lbtoxgrade_defs %>%
    select(TOXTERM, TOXGRD, INDICATR)
) %>%
  mutate(
    ATOXDSCLH = TOXTERM,
    ATOXGRLH = paste("Grade", TOXGRD)
  ) %>%
  rename(ATOXDIR = INDICATR) %>%
  select(ATOXDSCLH, ATOXGRLH, ATOXDIR)

# Process Data:

adsl <- pharmaverseadamjnj::adsl %>%
  filter(.data[[popfl]] == "Y") %>%
  select(USUBJID, all_of(c(popfl, trtvar, subgrpvar)))

adsl$colspan_trt <- factor(
  ifelse(adsl[[trtvar]] == ctrl_grp, " ", "Active Study Agent"),
  levels = c("Active Study Agent", " ")
)

adsl$rrisk_header <- "Risk Difference (%) (95% CI)"
adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp))

colspan_trt_map <- create_colspan_map(
  adsl,
  non_active_grp = ctrl_grp,
  non_active_grp_span_lbl = " ",
  active_grp_span_lbl = "Active Study Agent",
  colspan_var = "colspan_trt",
  trt_var = trtvar
)

ref_path <- c("colspan_trt", " ", trtvar, ctrl_grp)


## for checking row_counts on AGEGR1 : should consist with counts from ADSL
adsl_agegr1 <- adsl %>%
  select(all_of(c(trtvar, subgrpvar))) %>%
  group_by(across(all_of(c(trtvar, subgrpvar)))) %>%
  summarise(n = n())

adlb00 <- adlb_complete %>%
  select(
    USUBJID,
    AVISITN,
    AVISIT,
    starts_with("PAR"),
    starts_with("ATOX"),
    starts_with("ANL"),
    ONTRTFL,
    TRTEMFL,
    AVAL,
    APOBLFL,
    ABLFL,
    LVOTFL
  ) %>%
  inner_join(adsl) %>%
  mutate(
    ATOXGRL = as.character(ATOXGRL),
    ATOXGRH = as.character(ATOXGRH)
  ) %>%
  relocate(
    .,
    USUBJID,
    all_of(subgrpvar),
    ANL04FL,
    ANL05FL,
    ONTRTFL,
    TRTEMFL,
    AVISIT,
    ATOXGRL,
    ATOXGRH,
    ATOXDSCL,
    ATOXDSCH,
    PARAMCD,
    AVISIT,
    AVAL,
    APOBLFL,
    ABLFL
  )


adlb00 <- adlb00 # %>%
## APT comment on PARCAT6 :
## HGB and WBC : Set to "Blood and lymphatic system disorders".
## HGB and WBC parameter are in 2 categories, one for the high and another one for the low direction grading.
## Anemia (HGB low) and Leukocytosis (WBC high) are in the category "Blood and lymphatic system disorders".
## The grading in the opposite directions are categorized under "Investigations".
## Therefor, both PARCAT5 and PARCAT6 are populated for HGB abd WBC.
## Deal with what is needed at later level, when we have splitted low and high
# mutate(PARCAT56 = coalesce(PARCAT6,PARCAT5)) %>%
# mutate(PARCAT56 = factor(PARCAT56,levels=unique(c(levels(adlb_complete$PARCAT6),levels(adlb_complete$PARCAT5)))))

# obj_label(adlb00$PARCAT56) <- "Combined PARCAT56"

### important: previous actions lost the label of variables

adlb00 <- var_relabel_list(adlb00, var_labels(adlb_complete, fill = T))

parcat <- unique(
  adlb00 %>%
    select(starts_with("PARCAT"), PARAMCD, PARAM, ATOXDSCL, ATOXDSCH) %>%
    filter(!(is.na(PARCAT5) & is.na(PARCAT6)))
)


### data preparation

if (all(selparcat56 != "")) {
  filtered_adlb <- adlb00 %>%
    filter((PARCAT5 %in% selparcat56) | (PARCAT6 %in% selparcat56))
}


### low grades : ATOXDSCL ATOXGRL ANL04FL
### Note on Worst On-treatment
### note: by filter ANL04FL/ANL05FL, this table is restricted to On-treatment values, per definition of ANL04FL/ANL05FL
### therefor, no need to add ONTRTFL in filter
### if derivation of ANL04FL/ANL05FL is not restricted to ONTRTFL records, adding ONTRTFL here will not give the correct answer either
### as mixing worst with other period is not giving the proper selection !!!

filtered_adlb_low <- filtered_adlb %>%
  filter(ANL04FL == "Y" & !is.na(ATOXDSCL) & !is.na(ATOXGRL)) %>%
  mutate(
    ATOXDSCLH = ATOXDSCL,
    ATOXGRLH = ATOXGRL,
    ATOXDIR = "LOW"
  ) %>%
  select(USUBJID, starts_with("PAR"), starts_with("ATOX"), TRTEMFL) %>%
  select(-c(ATOXGRL, ATOXGRH, ATOXDSCL, ATOXDSCH))

### high grades: ATOXDSCH ATOXGRH ANL05FL
filtered_adlb_high <- filtered_adlb %>%
  filter(ANL05FL == "Y" & !is.na(ATOXDSCH) & !is.na(ATOXGRH)) %>%
  mutate(
    ATOXDSCLH = ATOXDSCH,
    ATOXGRLH = ATOXGRH,
    ATOXDIR = "HIGH"
  ) %>%
  select(USUBJID, starts_with("PAR"), starts_with("ATOX"), TRTEMFL) %>%
  select(-c(ATOXGRL, ATOXGRH, ATOXDSCL, ATOXDSCH))

## combine Low and high into adlb_tox
filtered_adlb_tox <-
  bind_rows(
    filtered_adlb_low,
    filtered_adlb_high
  ) %>%
  select(-c(ATOXGR, ATOXGRN)) %>%
  inner_join(adsl)

### correction of proper category (PARCAT56) for HGB (LOW) and WBC (HIGH)
filtered_adlb_tox <-
  filtered_adlb_tox %>%
  mutate(
    PARCAT56 = case_when(
      PARAMCD == "HGB" & ATOXDIR == "LOW" ~ PARCAT6,
      PARAMCD == "WBC" & ATOXDIR == "HIGH" ~ PARCAT6,

      ### fix on synthetic data !!!!
      PARAMCD == "WBC" & ATOXDIR == "LOW" ~ "Investigations",
      TRUE ~ PARCAT5
    )
  ) %>%
  mutate(
    PARCAT56 = factor(
      PARCAT56,
      levels = unique(c(
        "Blood and lymphatic system disorders",
        levels(adlb_complete$PARCAT5)
      ))
    )
  )


#### DO NOT USE TRTEMFL = Y in filter, as this will remove subjects from both numerator and denominator
#### instead : set ATOXGRLH to a non-reportable value (ie Grade 0) and keep in dataset
if (trtemfl) {
  filtered_adlb_tox <- filtered_adlb_tox %>%
    mutate(
      ATOXGRLH = case_when(
        is.na(TRTEMFL) | TRTEMFL != "Y" ~ "0",
        TRUE ~ ATOXGRLH
      )
    )
}


## convert some to factors -- lty will fail if these are not factors
filtered_adlb_tox <-
  filtered_adlb_tox %>%
  mutate(
    ATOXGRLH = factor(paste("Grade", ATOXGRLH), levels = paste("Grade", 0:5)),
    ATOXDIR = factor(ATOXDIR, levels = c("LOW", "HIGH"))
  )


filtered_adlb_tox <- unique(
  filtered_adlb_tox
)

check_non_unique_subject <- filtered_adlb_tox %>%
  group_by(USUBJID, PARAMCD, ATOXDSCLH) %>%
  summarize(n_subject = n()) %>%
  filter(n_subject > 1)

if (nrow(check_non_unique_subject)) {
  message(
    "Please review your data selection process, subject has multiple records"
  )
}


params <- unique(
  filtered_adlb_tox %>% select(PARCAT56, PARAMCD, PARAM, ATOXDSCLH, ATOXDIR)
)

all_params <- unique(
  adlb_complete %>%
    filter(!(is.na(PARCAT5) & is.na(PARCAT6))) %>%
    select(PARCAT5, PARCAT6, PARAMCD, PARAM, ATOXDSCL, ATOXDSCH)
)

### add relevant extra vars to lbtoxgrade_defs, only restrict to those actually in trial
### Neutrophil Count Decreased (NEUTSG NEUT) is causing for the many-to-many warning
lbtoxgrade_defs <- lbtoxgrade_defs %>%
  inner_join(
    .,
    unique(
      filtered_adlb_tox %>%
        select(PARAMCD, PARAM, ATOXDIR, ATOXDSCLH, PARCAT5, PARCAT6, PARCAT56)
    )
  )


### Define param_map to be used in layout
param_map <- lbtoxgrade_defs %>%
  select(PARCAT56, PARAM, PARAMCD, ATOXDIR, ATOXDSCLH, ATOXGRLH) %>%
  ### for proper sorting: add factor levels to PARAMCD, ATOXDIR
  mutate(
    PARAMCD = factor(PARAMCD, levels = levels(adlb00$PARAMCD)),
    ATOXDIR = factor(ATOXDIR, levels = c("LOW", "HIGH"))
  ) %>%
  # ### actual sorting
  #   arrange(PARCAT56,PARAMCD,ATOXDIR,ATOXGRLH) %>%
  ### actual sorting -- all alphabetic on output
  arrange(PARCAT56, ATOXDSCLH) %>%
  ### !!!! no factors are allowed in this split_fun map definition
  mutate(
    PARCAT56 = as.character(PARCAT56),
    PARAMCD = as.character(PARAMCD),
    PARAM = as.character(PARAM),
    ATOXDIR = as.character(ATOXDIR),
    ATOXDSCLH = as.character(ATOXDSCLH)
  ) # %>%
### !!!! do not remove Grade 0 here, as this would lead to incorrect N and % derivation
### filter(ATOXGRLH != "Grade 0")
### Grade 0 will be removed as a post-processing step

# Define layout and build table:

extra_args_rr <- list(
  method = "wald",
  denom = "n_df",
  ref_path = ref_path,
  .stats = c("denom", "count_unique_fraction"),
  denom_by = subgrpvar
)
extra_args_rr2 <- list(
  method = "wald",
  denom = "n_df",
  ref_path = ref_path,
  .stats = c("denom", "count_unique_denom_fraction"),
  denom_by = subgrpvar
)


lyt0 <- basic_table(show_colcounts = TRUE, colcount_format = "N=xx") %>%
  ### first columns
  split_cols_by(
    "colspan_trt",
    split_fun = trim_levels_to_map(map = colspan_trt_map)
  ) %>%
  split_cols_by(trtvar) %>%
  split_cols_by("rrisk_header", nested = FALSE) %>%
  split_cols_by(
    trtvar,
    labels_var = "rrisk_label",
    split_fun = remove_split_levels(ctrl_grp)
  ) %>%
  split_rows_by(
    subgrpvar,
    label_pos = "hidden",
    section_div = " ",
    split_fun = drop_split_levels,
    page_by = page_by
  ) %>%
  ###
  summarize_row_groups(
    var = subgrpvar,
    cfun = a_freq_j,
    extra_args = list(
      label_fstr = subgrplbl,
      denom = "n_altdf",
      denom_by = subgrpvar,
      riskdiff = FALSE,
      extrablankline = TRUE,
      .stats = c("n_altdf")
    )
  ) %>%
  split_rows_by(
    "PARCAT56",
    label_pos = "topleft",
    child_labels = "visible",
    split_label = "NCI-CTCAE Category",
    ### trim_levels_to_map needs to be applied at ALL split_rows_by levels
    split_fun = trim_levels_to_map(param_map),
    section_div = " ",
    indent_mod = indent_adj,
  ) %>%
  split_rows_by(
    "ATOXDSCLH",
    label_pos = "topleft",
    child_labels = "visible",
    split_label = "Laboratory Test",
    ### trim_levels_to_map needs to be applied at ALL split_rows_by levels
    split_fun = trim_levels_to_map(param_map),
    section_div = " "
  ) %>%
  append_topleft("    Grade, n (%)")

# version without explicit denominator (as in shell)
lyt <- lyt0 %>%
  # for testing, it is sometimes convenient to explicitely show the used denominator
  analyze(
    "ATOXGRLH",
    a_freq_j,
    extra_args = extra_args_rr,
    show_labels = "hidden",
    indent_mod = 0L
  )

result <- build_table(lyt, filtered_adlb_tox, alt_counts_df = adsl)

# version with explicit denominator (for verification)
lyt2 <- lyt0 %>%
  # for testing, it is sometimes convenient to explicitely show the used denominator
  analyze(
    "ATOXGRLH",
    a_freq_j,
    extra_args = extra_args_rr2,
    show_labels = "visible",
    indent_mod = 0L
  )

### apply layout
result2 <- build_table(lyt2, filtered_adlb_tox, alt_counts_df = adsl)

# Post-Processing:

remove_grade0 <- function(tr) {
  if (is(tr, "DataRow") & (tr@label == "Grade 0")) {
    return(FALSE)
  } else {
    return(TRUE)
  }
}

result <- result %>% prune_table(prune_func = keep_rows(remove_grade0))
result2 <- result2 %>% prune_table(prune_func = keep_rows(remove_grade0))

# Remove colcount from rrisk_header:

result <- remove_col_count(result)
result2 <- remove_col_count(result2)

# Add titles and footnotes:

result <- set_titles(result, titles)

# Convert to tbl file and output table

tt_to_tlgrtf(string_map = string_map, tt = result, file = fileid, orientation = "landscape")

TSFLAB03a: Subjects With =1 Laboratory Values With Elevated or Low Values Based on Worst On-treatment Value Using NCI-CTCAE Criteria by [Subgroup]; Safety Analysis Set (Study jjcs - core)
NCI-CTCAE Category	Active Study Agent			Risk Difference (%) (95% CI)
Laboratory Test	Xanomeline High Dose	Xanomeline Low Dose	Placebo	Xanomeline High Dose vs Placebo	Xanomeline Low Dose vs Placebo
Grade, n (%)	N=53	N=73	N=59
Age: ≥18 to <65 years	8	5	7

Blood and lymphatic system disorders
Anemia
N	8	5	7
Grade 1	7 (87.5%)	5 (100.0%)	5 (71.4%)	16.1 (-24.5, 56.6)	28.6 (-4.9, 62.0)
Grade 2	5 (62.5%)	5 (100.0%)	2 (28.6%)	33.9 (-13.5, 81.3)	71.4 (38.0, 100.0)
Grade 3	2 (25.0%)	2 (40.0%)	0	25.0 (-5.0, 55.0)	40.0 (-2.9, 82.9)

Leukocytosis
N	8	5	7
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Metabolism and nutritional disorders
Hyperkalemia
N	8	5	7
Grade 1	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 2	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 4	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Hypernatremia
N	8	5	7
Grade 1	0	0	1 (14.3%)	-14.3 (-40.2, 11.6)	-14.3 (-40.2, 11.6)
Grade 2	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 4	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Hypoalbuminemia
N	8	5	7
Grade 1	8 (100.0%)	4 (80.0%)	6 (85.7%)	14.3 (-11.6, 40.2)	-5.7 (-49.3, 37.9)
Grade 2	7 (87.5%)	4 (80.0%)	6 (85.7%)	1.8 (-32.8, 36.4)	-5.7 (-49.3, 37.9)
Grade 3	2 (25.0%)	0	3 (42.9%)	-17.9 (-65.2, 29.5)	-42.9 (-79.5, -6.2)

Hypoglycemia
N	8	5	7
Grade 1	4 (50.0%)	3 (60.0%)	7 (100.0%)	-50.0 (-84.6, -15.4)	-40.0 (-82.9, 2.9)
Grade 2	6 (75.0%)	2 (40.0%)	6 (85.7%)	-10.7 (-50.4, 28.9)	-45.7 (-95.9, 4.4)
Grade 3	5 (62.5%)	3 (60.0%)	3 (42.9%)	19.6 (-30.0, 69.3)	17.1 (-39.3, 73.6)
Grade 4	2 (25.0%)	0	1 (14.3%)	10.7 (-28.9, 50.4)	-14.3 (-40.2, 11.6)

Hypokalemia
N	8	5	7
Grade 2	4 (50.0%)	5 (100.0%)	5 (71.4%)	-21.4 (-69.6, 26.7)	28.6 (-4.9, 62.0)
Grade 3	2 (25.0%)	2 (40.0%)	3 (42.9%)	-17.9 (-65.2, 29.5)	-2.9 (-59.3, 53.6)
Grade 4	1 (12.5%)	0	0	12.5 (-10.4, 35.4)	0.0 (0.0, 0.0)

Hyponatremia
N	8	5	7
Grade 1	7 (87.5%)	4 (80.0%)	5 (71.4%)	16.1 (-24.5, 56.6)	8.6 (-39.9, 57.0)
Grade 3	3 (37.5%)	1 (20.0%)	3 (42.9%)	-5.4 (-55.0, 44.3)	-22.9 (-73.6, 27.9)
Grade 4	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Age: ≥65 to <75 years	16	17	19

Blood and lymphatic system disorders
Anemia
N	16	17	19
Grade 1	11 (68.8%)	10 (58.8%)	17 (89.5%)	-20.7 (-47.3, 5.9)	-30.7 (-57.8, -3.5)
Grade 2	11 (68.8%)	8 (47.1%)	13 (68.4%)	0.3 (-30.5, 31.2)	-21.4 (-53.0, 10.3)
Grade 3	9 (56.2%)	5 (29.4%)	9 (47.4%)	8.9 (-24.2, 42.0)	-18.0 (-49.2, 13.2)

Leukocytosis
N	16	17	19
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Metabolism and nutritional disorders
Hyperkalemia
N	16	17	19
Grade 1	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 2	1 (6.2%)	0	0	6.2 (-5.6, 18.1)	0.0 (0.0, 0.0)
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 4	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Hypernatremia
N	16	17	19
Grade 1	2 (12.5%)	2 (11.8%)	5 (26.3%)	-13.8 (-39.4, 11.8)	-14.6 (-39.6, 10.5)
Grade 2	1 (6.2%)	0	0	6.2 (-5.6, 18.1)	0.0 (0.0, 0.0)
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 4	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Hypoalbuminemia
N	16	17	19
Grade 1	15 (93.8%)	10 (58.8%)	18 (94.7%)	-1.0 (-16.5, 14.6)	-35.9 (-61.4, -10.5)
Grade 2	10 (62.5%)	6 (35.3%)	14 (73.7%)	-11.2 (-42.1, 19.7)	-38.4 (-68.5, -8.3)
Grade 3	7 (43.8%)	4 (23.5%)	6 (31.6%)	12.2 (-19.9, 44.2)	-8.0 (-37.1, 21.0)

Hypoglycemia
N	16	17	19
Grade 1	13 (81.2%)	11 (64.7%)	18 (94.7%)	-13.5 (-35.1, 8.1)	-30.0 (-54.9, -5.2)
Grade 2	11 (68.8%)	8 (47.1%)	10 (52.6%)	16.1 (-15.8, 48.1)	-5.6 (-38.2, 27.1)
Grade 3	7 (43.8%)	5 (29.4%)	6 (31.6%)	12.2 (-19.9, 44.2)	-2.2 (-32.3, 27.9)
Grade 4	1 (6.2%)	2 (11.8%)	0	6.2 (-5.6, 18.1)	11.8 (-3.6, 27.1)

Hypokalemia
N	16	16	19
Grade 2	12 (75.0%)	5 (31.2%)	11 (57.9%)	17.1 (-13.6, 47.8)	-26.6 (-58.4, 5.1)
Grade 3	6 (37.5%)	4 (25.0%)	10 (52.6%)	-15.1 (-47.8, 17.5)	-27.6 (-58.5, 3.3)
Grade 4	1 (6.2%)	1 (6.2%)	2 (10.5%)	-4.3 (-22.5, 13.9)	-4.3 (-22.5, 13.9)

Hyponatremia
N	16	15	19
Grade 1	14 (87.5%)	11 (73.3%)	17 (89.5%)	-2.0 (-23.3, 19.3)	-16.1 (-42.4, 10.2)
Grade 3	5 (31.2%)	8 (53.3%)	7 (36.8%)	-5.6 (-37.0, 25.8)	16.5 (-16.8, 49.8)
Grade 4	2 (12.5%)	1 (6.7%)	0	12.5 (-3.7, 28.7)	6.7 (-6.0, 19.3)
Age: ≥75 years	29	51	33

Blood and lymphatic system disorders
Anemia
N	29	51	33
Grade 1	24 (82.8%)	41 (80.4%)	29 (87.9%)	-5.1 (-22.8, 12.6)	-7.5 (-23.1, 8.1)
Grade 2	19 (65.5%)	25 (49.0%)	21 (63.6%)	1.9 (-22.0, 25.7)	-14.6 (-36.0, 6.8)
Grade 3	10 (34.5%)	23 (45.1%)	12 (36.4%)	-1.9 (-25.7, 22.0)	8.7 (-12.6, 30.1)

Leukocytosis
N	29	51	33
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Metabolism and nutritional disorders
Hyperkalemia
N	29	51	33
Grade 1	1 (3.4%)	0	1 (3.0%)	0.4 (-8.4, 9.3)	-3.0 (-8.9, 2.8)
Grade 2	0	0	1 (3.0%)	-3.0 (-8.9, 2.8)	-3.0 (-8.9, 2.8)
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 4	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Hypernatremia
N	29	51	33
Grade 1	5 (17.2%)	4 (7.8%)	4 (12.1%)	5.1 (-12.6, 22.8)	-4.3 (-17.6, 9.1)
Grade 2	1 (3.4%)	0	0	3.4 (-3.2, 10.1)	0.0 (0.0, 0.0)
Grade 3	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Grade 4	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Hypoalbuminemia
N	29	51	33
Grade 1	25 (86.2%)	38 (74.5%)	25 (75.8%)	10.4 (-8.8, 29.7)	-1.2 (-20.1, 17.6)
Grade 2	17 (58.6%)	31 (60.8%)	20 (60.6%)	-2.0 (-26.5, 22.5)	0.2 (-21.2, 21.6)
Grade 3	14 (48.3%)	15 (29.4%)	12 (36.4%)	11.9 (-12.6, 36.4)	-7.0 (-27.6, 13.7)

Hypoglycemia
N	29	50	33
Grade 1	24 (82.8%)	36 (72.0%)	30 (90.9%)	-8.2 (-25.0, 8.7)	-18.9 (-34.8, -3.1)
Grade 2	21 (72.4%)	25 (50.0%)	21 (63.6%)	8.8 (-14.3, 31.9)	-13.6 (-35.1, 7.8)
Grade 3	9 (31.0%)	17 (34.0%)	14 (42.4%)	-11.4 (-35.2, 12.4)	-8.4 (-29.8, 12.9)
Grade 4	2 (6.9%)	0	2 (6.1%)	0.8 (-11.5, 13.1)	-6.1 (-14.2, 2.1)

Hypokalemia
N	29	51	31
Grade 2	22 (75.9%)	28 (54.9%)	22 (71.0%)	4.9 (-17.4, 27.2)	-16.1 (-37.1, 5.0)
Grade 3	17 (58.6%)	17 (33.3%)	15 (48.4%)	10.2 (-14.9, 35.3)	-15.1 (-36.9, 6.8)
Grade 4	3 (10.3%)	5 (9.8%)	2 (6.5%)	3.9 (-10.2, 18.0)	3.4 (-8.5, 15.2)

Hyponatremia
N	29	51	33
Grade 1	26 (89.7%)	38 (74.5%)	29 (87.9%)	1.8 (-13.9, 17.5)	-13.4 (-29.7, 3.0)
Grade 3	13 (44.8%)	19 (37.3%)	15 (45.5%)	-0.6 (-25.5, 24.2)	-8.2 (-29.8, 13.4)
Grade 4	3 (10.3%)	0	0	10.3 (-0.7, 21.4)	0.0 (0.0, 0.0)

Note: On-treatment is defined as treatment-emergentlaboratory values obtained after the first dose and within [30 days] following treatment discontinuation. [Treatment-emergent values are those that worsened from baseline.]
Note: NCI-CTCAE grades (version 5.0.) are based on the laboratory result and do not take into account the clinical component, if applicable.

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