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
s_count_occurrences(
df,
denom = c("N_col", "n"),
.N_col,
.df_row,
drop = TRUE,
.var = "MHDECOD",
id = "USUBJID"
)
a_count_occurrences(
df,
denom = c("N_col", "n"),
.N_col,
.df_row,
drop = TRUE,
.var = "MHDECOD",
id = "USUBJID"
)
count_occurrences(
lyt,
vars,
var_labels = vars,
show_labels = "hidden",
...,
table_names = vars,
.stats = "count_fraction",
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
Arguments
- 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 rowsn
: number of patients with any occurrences
- .N_col
(
count
)
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is passed byrtables
.- .df_row
(
data.frame
)
data frame across all of the columns for the given row split.- drop
(
flag
)
should non appearing occurrence levels be dropped from the resulting table. Note that in that case the remaining occurrence levels in the table are sorted alphabetically.- .var
(
string
)
single variable name that is passed byrtables
when requested by a statistics function.- id
(
string
)
subject variable name.- lyt
(
layout
)
input layout where analyses will be added to.- vars
(
character
)
variable names for the primary analysis variable to be iterated over.- var_labels
(
character
)
character for label.- show_labels
(
string
)
label visibility: one of "default", "visible" and "hidden".- ...
additional arguments for the lower level functions.
- table_names
(
character
)
this can be customized in case that the samevars
are analyzed multiple times, to avoid warnings fromrtables
.- .stats
(
character
)
statistics to select for the table.- .formats
(named
character
orlist
)
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_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.
a_count_occurrences()
returns the corresponding list with formattedrtables::CellValue()
.
count_occurrences()
returns a layout object suitable for passing to further layouting functions, or tortables::build_table()
. Adding this function to anrtable
layout will add formatted rows containing the statistics froms_count_occurrences()
to the table layout.
Functions
s_count_occurrences()
: Statistics function which counts number of patients that report an occurrence.a_count_occurrences()
: Formatted analysis function which is used asafun
incount_occurrences()
.count_occurrences()
: Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper forrtables::analyze()
.
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
df <- data.frame(
USUBJID = as.character(c(1, 1, 2, 4, 4, 4)),
MHDECOD = c("MH1", "MH2", "MH1", "MH1", "MH1", "MH3")
)
N_per_col <- 4L
# Count unique occurrences per subject.
s_count_occurrences(
df,
.N_col = N_per_col,
.df_row = df,
.var = "MHDECOD",
id = "USUBJID"
)
#> $count
#> $count$MH1
#> [1] 3
#>
#> $count$MH2
#> [1] 1
#>
#> $count$MH3
#> [1] 1
#>
#>
#> $count_fraction
#> $count_fraction$MH1
#> [1] 3.00 0.75
#>
#> $count_fraction$MH2
#> [1] 1.00 0.25
#>
#> $count_fraction$MH3
#> [1] 1.00 0.25
#>
#>
#> $fraction
#> $fraction$MH1
#> num denom
#> 3 4
#>
#> $fraction$MH2
#> num denom
#> 1 4
#>
#> $fraction$MH3
#> num denom
#> 1 4
#>
#>
# We need to ungroup `count_fraction` first so that the `rtables` formatting
# function `format_count_fraction()` can be applied correctly.
afun <- make_afun(a_count_occurrences, .ungroup_stats = c("count", "count_fraction", "fraction"))
afun(
df,
.N_col = N_per_col,
.df_row = df,
.var = "MHDECOD",
id = "USUBJID"
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod row_label
#> 1 MH1 3 0 MH1
#> 2 MH2 1 0 MH2
#> 3 MH3 1 0 MH3
#> 4 MH1 3 (75.0%) 0 MH1
#> 5 MH2 1 (25.0%) 0 MH2
#> 6 MH3 1 (25.0%) 0 MH3
#> 7 MH1 3/4 (75.0%) 0 MH1
#> 8 MH2 1/4 (25.0%) 0 MH2
#> 9 MH3 1/4 (25.0%) 0 MH3
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)
)
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
lyt %>%
build_table(df, alt_counts_df = df_adsl) %>%
prune_table()
#> 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%)