Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline

Patient count and fraction for laboratory events (worsen from baseline) shift table.

Usage

s_count_abnormal_lab_worsen_by_baseline(
  df,
  .var = "ATOXGR",
  variables = list(id = "USUBJID", baseline_var = "BTOXGR", direction_var = "GRADDR")
)

a_count_abnormal_lab_worsen_by_baseline(
  df,
  .var = "ATOXGR",
  variables = list(id = "USUBJID", baseline_var = "BTOXGR", direction_var = "GRADDR")
)

count_abnormal_lab_worsen_by_baseline(
  lyt,
  var,
  ...,
  table_names = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

df

(data.frame)
data set containing all analysis variables.

.var, var

(string)
single variable name that is passed by rtables when requested by a statistics function.

variables

(named list of string)
list of additional analysis variables including:

id (string)
subject variable name.
baseline_var (string)
name of the data column containing baseline toxicity variable.
direction_var (string)
see direction_var for more details.

lyt

(layout)
input layout where analyses will be added to.

...

additional arguments for the lower level functions.

table_names

(character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels.

Value

s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any".

a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with formatted rtables::CellValue().

Functions

s_count_abnormal_lab_worsen_by_baseline(): Statistics function which calculates the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any".
a_count_abnormal_lab_worsen_by_baseline():
count_abnormal_lab_worsen_by_baseline(): Layout creating function which can be used for creating tables, which can take statistics function arguments and additional format arguments (see below).

Examples

library(dplyr)

# The direction variable, GRADDR, is based on metadata
adlb <- tern_ex_adlb %>%
  mutate(
    GRADDR = case_when(
      PARAMCD == "ALT" ~ "B",
      PARAMCD == "CRP" ~ "L",
      PARAMCD == "IGA" ~ "H"
    )
  ) %>%
  filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")

df <- h_adlb_worsen(
  adlb,
  worst_flag_low = c("WGRLOFL" = "Y"),
  worst_flag_high = c("WGRHIFL" = "Y"),
  direction_var = "GRADDR"
)
# Internal function - s_count_abnormal_lab_worsen_by_baseline
if (FALSE) {
# Patients with worsening lab grade for CRP in the direction of low
s_count_abnormal_lab_worsen_by_baseline(
  df = df %>% filter(ARMCD == "ARM A" & PARAMCD == "CRP"),
  .var = "ATOXGR",
  variables = list(
    id = "USUBJID",
    baseline_var = "BTOXGR",
    direction_var = "GRADDR"
  )
)
}

# Internal function - a_count_abnormal_lab_worsen_by_baseline
if (FALSE) {
a_count_abnormal_lab_worsen_by_baseline(
  df = df %>% filter(ARMCD == "ARM A" & PARAMCD == "CRP"),
  .var = "ATOXGR",
  variables = list(id = "USUBJID", baseline_var = "BTOXGR", direction_var = "GRADDR")
)
}

basic_table() %>%
  split_cols_by("ARMCD") %>%
  add_colcounts() %>%
  split_rows_by("PARAMCD") %>%
  split_rows_by("GRADDR") %>%
  count_abnormal_lab_worsen_by_baseline(
    var = "ATOXGR",
    variables = list(
      id = "USUBJID",
      baseline_var = "BTOXGR",
      direction_var = "GRADDR"
    )
  ) %>%
  append_topleft("Direction of Abnormality") %>%
  build_table(df = df, alt_counts_df = tern_ex_adsl)
#> Direction of Abnormality       ARM A           ARM B           ARM C    
#>                               (N=69)          (N=73)          (N=58)    
#> ————————————————————————————————————————————————————————————————————————
#> IGA                                                                     
#>   High                                                                  
#>     1                       6/63 (9.5%)     6/64 (9.4%)      4/50 (8%)  
#>     2                      8/64 (12.5%)     5/67 (7.5%)    8/53 (15.1%) 
#>     3                      7/66 (10.6%)     5/68 (7.4%)    9/57 (15.8%) 
#>     4                       6/68 (8.8%)     2/72 (2.8%)     3/58 (5.2%) 
#>     Any                    27/68 (39.7%)    18/72 (25%)    24/58 (41.4%)
#> ALT                                                                     
#>   High                                                                  
#>     1                      7/62 (11.3%)     6/62 (9.7%)     2/48 (4.2%) 
#>     2                      12/62 (19.4%)     4/67 (6%)      11/50 (22%) 
#>     3                       4/64 (6.2%)    11/71 (15.5%)   7/56 (12.5%) 
#>     4                       1/66 (1.5%)    8/71 (11.3%)      4/57 (7%)  
#>     Any                    24/66 (36.4%)   29/71 (40.8%)   24/57 (42.1%)
#>   Low                                                                   
#>     1                      12/67 (17.9%)    4/66 (6.1%)    7/52 (13.5%) 
#>     2                      9/68 (13.2%)    12/69 (17.4%)   6/55 (10.9%) 
#>     3                       6/69 (8.7%)     4/71 (5.6%)     5/56 (8.9%) 
#>     4                      7/69 (10.1%)     7/73 (9.6%)    6/58 (10.3%) 
#>     Any                    34/69 (49.3%)    27/73 (37%)    24/58 (41.4%)
#> CRP                                                                     
#>   Low                                                                   
#>     1                      11/66 (16.7%)   10/67 (14.9%)    4/47 (8.5%) 
#>     2                      8/66 (12.1%)     1/70 (1.4%)     6/50 (12%)  
#>     3                       4/68 (5.9%)    9/70 (12.9%)     5/53 (9.4%) 
#>     4                      7/69 (10.1%)     6/72 (8.3%)     4/55 (7.3%) 
#>     Any                    30/69 (43.5%)   26/72 (36.1%)   19/55 (34.5%)