A function used for obtaining statistics within the columns of your table. Used in change from baseline tables. This takes the visit names as its row labels.
Arguments
- df
(
data.frameortibble)
dataset.- .var
(
string)
single variable name that is passed byrtables- .spl_context
(
data.frame)
gives information about ancestor split states.- .formats
(named
characterorlist)
formats for the statistics. When set to"default"formats_varargument will be used for format setup.- formats_var
(
stringorNULL)NULL(the default) or the name of the list column containing named lists of default formats to use. These will not override .formats (other than"default").- exclude_visits
Visits to exclude for the second/third part of the columns ("BASE", "CHG").
- var_names
(
character vector)
Vector of variable names to use instead of the default AVAL, CHG, BASE. By default, the function expects these specific variable names in your data, but you can customize them to match your dataset's column names.
Value
An analysis function (for use with rtables::analyze) implementing
the specified statistics.
Typically used in a layout that has been setup by stats_in_cols_setup()
Examples
# example code
library(dplyr)
advs <- ex_advs |>
filter(AVISIT %in% toupper(c("Screening", "Baseline", "Week 1 Day 8", "Week 2 Day 15"))) |>
mutate(AVISIT = droplevels(AVISIT)) |>
filter(PARAMCD %in% c("DIABP", "SYSBP"))
advs_fmt <- tribble(
~PARAMCD, ~fmt_d,
"DIABP", list(
mean = "xx.xx", sd = "xx.xxx", se = "xx.xxx", median = "xx.xx",
min = "xx.x", max = "xx.x", mean_sd = "xx.xx (xx.xxx)"
),
"SYSBP", list(
mean = "xx.x", sd = "xx.xx", se = "xx.xx", median = "xx.x",
min = "xx.", max = "xx.", mean_sd = "xx.x (xx.xx)"
)
)
advs <- left_join(advs, advs_fmt)
#> Joining with `by = join_by(PARAMCD)`
mysplitfun <- make_split_fun(
post = list(stats_in_cols_setup())
)
lyt <- basic_table() |>
split_cols_by_multivar(
c("AVAL", "BASE", "CHG"),
varlabels = c("Visit Values", " ", "Change from Baseline")
) |>
split_cols_by("STUDYID", split_fun = mysplitfun) |>
split_rows_by("PARAM", split_fun = drop_split_levels) |>
split_rows_by("ARMCD", labels_var = "ARM") |>
analyze("AVISIT", afun = column_stats, extra_args = list(
exclude_visits = toupper(c("Baseline", "Screening")),
.formats = "default",
formats_var = "fmt_d"
))
result <- build_table(lyt, advs, round_type = "sas")
head(result, 20)
#> Visit Values Change from Baseline
#> N Mean SD SE Med Min Max Base Mean (SD) N Mean SD SE Med Min Max
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Diastolic Blood Pressure
#> A: Drug X
#> SCREENING 134 49.97 7.197 0.622 49.71 31.7 71.2
#> BASELINE 134 48.60 7.962 0.688 48.42 27.7 64.6
#> WEEK 1 DAY 8 134 50.26 7.514 0.649 50.09 33.0 69.0 48.60 (7.962) 134 1.66 10.774 0.931 0.61 -20.4 29.9
#> WEEK 2 DAY 15 134 50.84 7.850 0.678 51.44 31.9 70.3 48.60 (7.962) 134 2.23 11.752 1.015 3.13 -23.9 39.2
#> B: Placebo
#> SCREENING 134 50.75 8.429 0.728 50.09 29.3 69.2
#> BASELINE 134 50.44 7.948 0.687 50.18 21.7 67.5
#> WEEK 1 DAY 8 134 49.67 7.743 0.669 49.75 33.7 66.5 50.44 (7.948) 134 -0.77 10.947 0.946 -1.11 -25.8 28.4
#> WEEK 2 DAY 15 134 49.72 8.445 0.730 50.23 30.6 68.1 50.44 (7.948) 134 -0.72 12.444 1.075 -0.71 -28.4 37.0
#> C: Combination
#> SCREENING 132 50.19 7.627 0.664 49.64 26.9 70.0
#> BASELINE 132 51.11 7.790 0.678 50.80 29.7 71.4
#> WEEK 1 DAY 8 132 48.86 7.888 0.687 47.68 30.4 67.0 51.11 (7.790) 132 -2.25 10.380 0.903 -2.23 -22.1 22.4
#> WEEK 2 DAY 15 132 49.98 8.338 0.726 51.01 24.8 65.6 51.11 (7.790) 132 -1.13 11.639 1.013 -2.27 -29.2 30.7
#> Systolic Blood Pressure
#> A: Drug X
#> SCREENING 134 49.6 8.32 0.72 49.6 24 71
#> BASELINE 134 49.4 8.47 0.73 48.3 24 71