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

count_abnormal_lab_worsen_by_baseline(
  lyt,
  var,
  variables = list(id = "USUBJID", baseline_var = "BTOXGR", direction_var = "GRADDR"),
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

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")
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

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.

na_str

(string)
string used to replace all NA or empty values in the output.

nested

(flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

...

additional arguments for the lower level functions.

table_names

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

.stats

(character)
statistics to select for the table. Run get_stats("abnormal_by_worst_grade_worsen") to see all available statistics.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

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

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

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.

Value

count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout.

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

count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades.
a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_lab_worsen_by_baseline().

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"
)

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)
#>                                ARM A           ARM B           ARM C    
#> Direction of Abnormality      (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                      7/63 (11.1%)     6/62 (9.7%)     2/48 (4.2%) 
#>     2                       12/63 (19%)      4/67 (6%)      11/50 (22%) 
#>     3                       4/65 (6.2%)    11/71 (15.5%)   7/56 (12.5%) 
#>     4                       1/67 (1.5%)    8/71 (11.3%)      4/57 (7%)  
#>     Any                    24/67 (35.8%)   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%)