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Count patients with abnormal range values

Source: R/abnormal.R
abnormal.Rd

[Stable]

The analyze function count_abnormal() creates a layout element to count patients with abnormal analysis range values in each direction.

This function analyzes primary analysis variable var which indicates abnormal range results. Additional analysis variables that can be supplied as a list via the variables parameter are id (defaults to USUBJID), a variable to indicate unique subject identifiers, and baseline (defaults to BNRIND), a variable to indicate baseline reference ranges.

For each direction specified via the abnormal parameter (e.g. High or Low), a fraction of patient counts is returned, with numerator and denominator calculated as follows:

  • num: The number of patients with this abnormality recorded while on treatment.

  • denom: The total number of patients with at least one post-baseline assessment.

This function assumes that df has been filtered to only include post-baseline records.

Usage 

count_abnormal(
  lyt,
  var,
  abnormal = list(Low = "LOW", High = "HIGH"),
  variables = list(id = "USUBJID", baseline = "BNRIND"),
  exclude_base_abn = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = var,
  .stats = "fraction",
  .stat_names = NULL,
  .formats = list(fraction = format_fraction),
  .labels = NULL,
  .indent_mods = NULL
)

s_count_abnormal(
  df,
  .var,
  abnormal = list(Low = "LOW", High = "HIGH"),
  variables = list(id = "USUBJID", baseline = "BNRIND"),
  exclude_base_abn = FALSE,
  ...
)

a_count_abnormal(
  df,
  ...,
  .stats = NULL,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

abnormal

(named list)
list identifying the abnormal range level(s) in var. Defaults to list(Low = "LOW", High = "HIGH") but you can also group different levels into the named list, for example, abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH")).

variables

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

exclude_base_abn

(flag)
whether to exclude subjects with baseline abnormality from numerator and denominator.

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.

Options are: 'fraction'

.stat_names

(character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producing rtables::as_result_df() with make_ard = TRUE.

.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() 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() to the table layout.

  • s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients.

Functions

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

  • s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level.

  • a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal().

Note

  • count_abnormal() only considers a single variable that contains multiple abnormal levels.

  • df should be filtered to only include post-baseline records.

  • The denominator includes patients that may have other abnormal levels at baseline, and patients missing baseline records. Patients with these abnormalities at baseline can be optionally excluded from numerator and denominator via the exclude_base_abn parameter.

Examples 

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:testthat’:
#> 
#>     matches
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

df <- data.frame(
  USUBJID = as.character(c(1, 1, 2, 2)),
  ANRIND = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
  BNRIND = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
  ONTRTFL = c("", "Y", "", "Y"),
  stringsAsFactors = FALSE
)

# Select only post-baseline records.
df <- df %>%
  filter(ONTRTFL == "Y")

# Layout creating function.
basic_table() %>%
  count_abnormal(var = "ANRIND", abnormal = list(high = "HIGH", low = "LOW")) %>%
  build_table(df)
#>         all obs 
#> ————————————————
#> high   1/2 (50%)
#> low    1/2 (50%)

# Passing of statistics function and formatting arguments.
df2 <- data.frame(
  ID = as.character(c(1, 1, 2, 2)),
  RANGE = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
  BL_RANGE = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
  ONTRTFL = c("", "Y", "", "Y"),
  stringsAsFactors = FALSE
)

# Select only post-baseline records.
df2 <- df2 %>%
  filter(ONTRTFL == "Y")

basic_table() %>%
  count_abnormal(
    var = "RANGE",
    abnormal = list(low = "LOW", high = "HIGH"),
    variables = list(id = "ID", baseline = "BL_RANGE")
  ) %>%
  build_table(df2)
#>         all obs 
#> ————————————————
#> low    1/2 (50%)
#> high   1/2 (50%)