Count Patients with Marked Laboratory Abnormalities

Primary analysis variable .var indicates whether single, replicated or last marked laboratory abnormality was observed (factor). Additional analysis variables are id (character or factor) and direction (factor) indicating the direction of the abnormality. Denominator is number of patients with at least one valid measurement during the analysis.

For Single, not last and Last or replicated: Numerator is number of patients with Single, not last and Last or replicated levels, respectively.
For Any: Numerator is the number of patients with either single or replicated marked abnormalities.

Usage

s_count_abnormal_by_marked(
  df,
  .var = "AVALCAT1",
  .spl_context,
  category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
  variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")
)

a_count_abnormal_by_marked(
  df,
  .var = "AVALCAT1",
  .spl_context,
  category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
  variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")
)

count_abnormal_by_marked(
  lyt,
  var,
  ...,
  .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.
.spl_context: (data.frame)
gives information about ancestor split states that is passed by rtables.
category: (list)
with different marked category names for single and last or replicated.
variables: (named list of string)
list of additional analysis variables.
lyt: (layout)
input layout where analyses will be added to.
...: additional arguments for the lower level functions.
.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. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

Value

s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results.

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

count_abnormal_by_marked() 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_marked() to the table layout.

Functions

s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities.
a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked().
count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

Note

Single, not last and Last or replicated levels are mutually exclusive. If a patient has abnormalities that meet both the Single, not last and Last or replicated criteria, then the patient will be counted only under the Last or replicated category.

Examples

library(dplyr)

df <- data.frame(
  USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))),
  ARMCD = factor(c(rep("ARM A", 5), rep("ARM B", 5), rep("ARM A", 5), rep("ARM B", 5))),
  ANRIND = factor(c(
    "NORMAL", "HIGH", "HIGH", "HIGH HIGH", "HIGH",
    "HIGH", "HIGH", "HIGH HIGH", "NORMAL", "HIGH HIGH", "NORMAL", "LOW", "LOW", "LOW LOW", "LOW",
    "LOW", "LOW", "LOW LOW", "NORMAL", "LOW LOW"
  )),
  ONTRTFL = rep(c("", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"), 2),
  PARAMCD = factor(c(rep("CRP", 10), rep("ALT", 10))),
  AVALCAT1 = factor(rep(c("", "", "", "SINGLE", "REPLICATED", "", "", "LAST", "", "SINGLE"), 2)),
  stringsAsFactors = FALSE
)

df <- df %>%
  mutate(abn_dir = factor(
    case_when(
      ANRIND == "LOW LOW" ~ "Low",
      ANRIND == "HIGH HIGH" ~ "High",
      TRUE ~ ""
    ),
    levels = c("Low", "High")
  ))

# Select only post-baseline records.
df <- df %>% filter(ONTRTFL == "Y")
df_crp <- df %>%
  filter(PARAMCD == "CRP") %>%
  droplevels()
full_parent_df <- list(df_crp, "not_needed")
cur_col_subset <- list(rep(TRUE, nrow(df_crp)), "not_needed")
spl_context <- data.frame(
  split = c("PARAMCD", "GRADE_DIR"),
  full_parent_df = I(full_parent_df),
  cur_col_subset = I(cur_col_subset)
)

map <- unique(
  df[df$abn_dir %in% c("Low", "High") & df$AVALCAT1 != "", c("PARAMCD", "abn_dir")]
) %>%
  lapply(as.character) %>%
  as.data.frame() %>%
  arrange(PARAMCD, abn_dir)

basic_table() %>%
  split_cols_by("ARMCD") %>%
  split_rows_by("PARAMCD") %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique_count"
  ) %>%
  split_rows_by(
    "abn_dir",
    split_fun = trim_levels_to_map(map)
  ) %>%
  count_abnormal_by_marked(
    var = "AVALCAT1",
    variables = list(
      id = "USUBJID",
      param = "PARAMCD",
      direction = "abn_dir"
    )
  ) %>%
  build_table(df = df)
#>                           ARM A      ARM B  
#> ————————————————————————————————————————————
#> ALT (n)                     1          1    
#>   Low                                       
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)
#> CRP (n)                     1          1    
#>   High                                      
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)

basic_table() %>%
  split_cols_by("ARMCD") %>%
  split_rows_by("PARAMCD") %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique_count"
  ) %>%
  split_rows_by(
    "abn_dir",
    split_fun = trim_levels_in_group("abn_dir")
  ) %>%
  count_abnormal_by_marked(
    var = "AVALCAT1",
    variables = list(
      id = "USUBJID",
      param = "PARAMCD",
      direction = "abn_dir"
    )
  ) %>%
  build_table(df = df)
#>                           ARM A      ARM B  
#> ————————————————————————————————————————————
#> ALT (n)                     1          1    
#>   Low                                       
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)
#> CRP (n)                     1          1    
#>   High                                      
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)