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[Stable]

Primary analysis variable .var indicates the toxicity grade (factor), and additional analysis variables are id (character or factor), param (factor) and grade_dir (factor). The pre-processing steps are crucial when using this function. For a certain direction (e.g. high or low) this function counts patients in the denominator as number of patients with at least one valid measurement during treatment, and patients in the numerator as follows:

  • 1 to 4: Numerator is number of patients with worst grades 1-4 respectively;

  • Any: Numerator is number of patients with at least one abnormality, which means grade is different from 0.

Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps.

Usage

count_abnormal_by_worst_grade(
  lyt,
  var,
  variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR"),
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_count_abnormal_by_worst_grade(
  df,
  .var = "GRADE_ANL",
  .spl_context,
  variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
)

a_count_abnormal_by_worst_grade(
  df,
  .var = "GRADE_ANL",
  .spl_context,
  variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

variables

(named list of string)
list of additional analysis variables.

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.

.stats

(character)
statistics to select for the table. Run get_stats("abnormal_by_worst_grade") to see available statistics for this function.

.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.

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

Value

  • count_abnormal_by_worst_grade() 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_by_worst_grade() to the table layout.

  • s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results.

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

Functions

  • count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

  • s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade.

  • a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade().

See also

h_adlb_abnormal_by_worst_grade() which pre-processes ADLB data frames to be used in count_abnormal_by_worst_grade().

Examples

library(dplyr)
library(forcats)
adlb <- tern_ex_adlb

# Data is modified in order to have some parameters with grades only in one direction
# and simulate the real data.
adlb$ATOXGR[adlb$PARAMCD == "ALT" & adlb$ATOXGR %in% c("1", "2", "3", "4")] <- "-1"
adlb$ANRIND[adlb$PARAMCD == "ALT" & adlb$ANRIND == "HIGH"] <- "LOW"
adlb$WGRHIFL[adlb$PARAMCD == "ALT"] <- ""

adlb$ATOXGR[adlb$PARAMCD == "IGA" & adlb$ATOXGR %in% c("-1", "-2", "-3", "-4")] <- "1"
adlb$ANRIND[adlb$PARAMCD == "IGA" & adlb$ANRIND == "LOW"] <- "HIGH"
adlb$WGRLOFL[adlb$PARAMCD == "IGA"] <- ""

# Pre-processing
adlb_f <- adlb %>% h_adlb_abnormal_by_worst_grade()

# Map excludes records without abnormal grade since they should not be displayed
# in the table.
map <- unique(adlb_f[adlb_f$GRADE_DIR != "ZERO", c("PARAM", "GRADE_DIR", "GRADE_ANL")]) %>%
  lapply(as.character) %>%
  as.data.frame() %>%
  arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)

basic_table() %>%
  split_cols_by("ARMCD") %>%
  split_rows_by("PARAM") %>%
  split_rows_by("GRADE_DIR", split_fun = trim_levels_to_map(map)) %>%
  count_abnormal_by_worst_grade(
    var = "GRADE_ANL",
    variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
  ) %>%
  build_table(df = adlb_f)
#>                                          ARM A        ARM B        ARM C   
#> ———————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement                                       
#>   LOW                                                                      
#>     1                                  12 (17.4%)    5 (6.8%)    8 (13.8%) 
#>     2                                   9 (13%)     13 (17.8%)   6 (10.3%) 
#>     3                                   6 (8.7%)     4 (5.5%)    6 (10.3%) 
#>     4                                  7 (10.1%)     7 (9.6%)    6 (10.3%) 
#>     Any                                34 (49.3%)   29 (39.7%)   26 (44.8%)
#> C-Reactive Protein Measurement                                             
#>   LOW                                                                      
#>     1                                  11 (15.9%)   12 (16.4%)   7 (12.1%) 
#>     2                                  8 (11.6%)     2 (2.7%)    6 (10.3%) 
#>     3                                   4 (5.8%)    9 (12.3%)    6 (10.3%) 
#>     4                                  7 (10.1%)     6 (8.2%)     4 (6.9%) 
#>     Any                                30 (43.5%)   29 (39.7%)   23 (39.7%)
#>   HIGH                                                                     
#>     1                                  8 (11.6%)    11 (15.1%)    2 (3.4%) 
#>     2                                   9 (13%)     11 (15.1%)    11 (19%) 
#>     3                                  14 (20.3%)   10 (13.7%)    5 (8.6%) 
#>     4                                   2 (2.9%)     4 (5.5%)    6 (10.3%) 
#>     Any                                33 (47.8%)   36 (49.3%)   24 (41.4%)
#> Immunoglobulin A Measurement                                               
#>   HIGH                                                                     
#>     1                                  7 (10.1%)     7 (9.6%)    6 (10.3%) 
#>     2                                  8 (11.6%)     6 (8.2%)    8 (13.8%) 
#>     3                                  7 (10.1%)     5 (6.8%)    9 (15.5%) 
#>     4                                   6 (8.7%)     2 (2.7%)     3 (5.2%) 
#>     Any                                28 (40.6%)   20 (27.4%)   26 (44.8%)