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Occurrence counts

Source: R/count_occurrences.R
count_occurrences.Rd

[Stable]

Functions for analyzing frequencies and fractions of occurrences for patients with occurrence data. Primary analysis variables are the dictionary terms. All occurrences are counted for total counts. Multiple occurrences within patient at the lowest term level displayed in the table are counted only once.

Usage 

count_occurrences(
  lyt,
  vars,
  id = "USUBJID",
  drop = TRUE,
  var_labels = vars,
  show_labels = "hidden",
  riskdiff = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = "count_fraction_fixed_dp",
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

summarize_occurrences(
  lyt,
  var,
  id = "USUBJID",
  drop = TRUE,
  riskdiff = FALSE,
  na_str = default_na_str(),
  ...,
  .stats = "count_fraction_fixed_dp",
  .formats = NULL,
  .indent_mods = NULL,
  .labels = NULL
)

s_count_occurrences(
  df,
  denom = c("N_col", "n"),
  .N_col,
  .df_row,
  drop = TRUE,
  .var = "MHDECOD",
  id = "USUBJID"
)

a_count_occurrences(
  df,
  labelstr = "",
  id = "USUBJID",
  denom = c("N_col", "n"),
  drop = TRUE,
  .N_col,
  .var = NULL,
  .df_row = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na_str = default_na_str()
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

id

(string)
subject variable name.

drop

(flag)
whether non-appearing occurrence levels should be dropped from the resulting table. Note that in that case the remaining occurrence levels in the table are sorted alphabetically.

var_labels

(character)
variable labels.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

riskdiff

(flag)
whether a risk difference column is present. When set to TRUE, add_riskdiff() must be used as split_fun in the prior column split of the table layout, specifying which columns should be compared. See stat_propdiff_ci() for details on risk difference calculation.

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("count_occurrences") 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.

denom

(string)
choice of denominator for patient proportions. Can be:

  • N_col: total number of patients in this column across rows

  • n: number of patients with any occurrences

.N_col

(integer(1))
column-wise N (column count) for the full column being analyzed that is typically passed by rtables.

.df_row

(data.frame)
data frame across all of the columns for the given row split.

.var, var

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

labelstr

(string)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.

Value

  • count_occurrences() 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_occurrences() to the table layout.

  • summarize_occurrences() 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 content rows containing the statistics from s_count_occurrences() to the table layout.

  • s_count_occurrences() returns a list with:

    • count: list of counts with one element per occurrence.

    • count_fraction: list of counts and fractions with one element per occurrence.

    • fraction: list of numerators and denominators with one element per occurrence.

Functions

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

  • summarize_occurrences(): Layout-creating function which can take content function arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().

  • s_count_occurrences(): Statistics function which counts number of patients that report an occurrence.

  • a_count_occurrences(): Formatted analysis function which is used as afun in count_occurrences().

Note

By default, occurrences which don't appear in a given row split are dropped from the table and the occurrences in the table are sorted alphabetically per row split. Therefore, the corresponding layout needs to use split_fun = drop_split_levels in the split_rows_by calls. Use drop = FALSE if you would like to show all occurrences.

Examples 

library(dplyr)
df <- data.frame(
  USUBJID = as.character(c(
    1, 1, 2, 4, 4, 4,
    6, 6, 6, 7, 7, 8
  )),
  MHDECOD = c(
    "MH1", "MH2", "MH1", "MH1", "MH1", "MH3",
    "MH2", "MH2", "MH3", "MH1", "MH2", "MH4"
  ),
  ARM = rep(c("A", "B"), each = 6),
  SEX = c("F", "F", "M", "M", "M", "M", "F", "F", "F", "M", "M", "F")
)
df_adsl <- df %>%
  select(USUBJID, ARM) %>%
  unique()

# Create table layout
lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_occurrences(vars = "MHDECOD", .stats = c("count_fraction"))

# Apply table layout to data and produce `rtable` object
tbl <- lyt %>%
  build_table(df, alt_counts_df = df_adsl) %>%
  prune_table()

tbl
#>           A           B    
#>         (N=3)       (N=3)  
#> ———————————————————————————
#> MH1   3 (100%)    1 (33.3%)
#> MH2   1 (33.3%)   2 (66.7%)
#> MH3   1 (33.3%)   1 (33.3%)
#> MH4       0       1 (33.3%)

# Layout creating function with custom format.
basic_table() %>%
  add_colcounts() %>%
  split_rows_by("SEX", child_labels = "visible") %>%
  summarize_occurrences(
    var = "MHDECOD",
    .formats = c("count_fraction" = "xx.xx (xx.xx%)")
  ) %>%
  build_table(df, alt_counts_df = df_adsl)
#>          all obs 
#>           (N=6)  
#> —————————————————
#> F                
#>   MH1   1 (16.7%)
#>   MH2   2 (33.3%)
#>   MH3   1 (16.7%)
#>   MH4   1 (16.7%)
#> M                
#>   MH1   3 (50.0%)
#>   MH2   1 (16.7%)
#>   MH3   1 (16.7%)

# Count unique occurrences per subject.
s_count_occurrences(
  df,
  .N_col = 4L,
  .df_row = df,
  .var = "MHDECOD",
  id = "USUBJID"
)
#> $count
#> $count$MH1
#> [1] 4
#> 
#> $count$MH2
#> [1] 3
#> 
#> $count$MH3
#> [1] 2
#> 
#> $count$MH4
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$MH1
#> [1] 4 1
#> 
#> $count_fraction$MH2
#> [1] 3.00 0.75
#> 
#> $count_fraction$MH3
#> [1] 2.0 0.5
#> 
#> $count_fraction$MH4
#> [1] 1.00 0.25
#> 
#> 
#> $fraction
#> $fraction$MH1
#>   num denom 
#>     4     4 
#> 
#> $fraction$MH2
#>   num denom 
#>     3     4 
#> 
#> $fraction$MH3
#>   num denom 
#>     2     4 
#> 
#> $fraction$MH4
#>   num denom 
#>     1     4 
#> 
#> 

a_count_occurrences(
  df,
  .N_col = 4L,
  .df_row = df,
  .var = "MHDECOD",
  id = "USUBJID"
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>    row_name formatted_cell indent_mod row_label
#> 1       MH1              4          0       MH1
#> 2       MH2              3          0       MH2
#> 3       MH3              2          0       MH3
#> 4       MH4              1          0       MH4
#> 5       MH1       4 (100%)          0       MH1
#> 6       MH2        3 (75%)          0       MH2
#> 7       MH3        2 (50%)          0       MH3
#> 8       MH4        1 (25%)          0       MH4
#> 9       MH1       4 (100%)          0       MH1
#> 10      MH2      3 (75.0%)          0       MH2
#> 11      MH3      2 (50.0%)          0       MH3
#> 12      MH4      1 (25.0%)          0       MH4
#> 13      MH1   4/4 (100.0%)          0       MH1
#> 14      MH2    3/4 (75.0%)          0       MH2
#> 15      MH3    2/4 (50.0%)          0       MH3
#> 16      MH4    1/4 (25.0%)          0       MH4