Suppose you need to create the table below, and need an ARD representation of the results to get started. Here, we will review an examples for creating a basic demographics table.
To get started, load the {cards} package.
Demographics
| Characteristic |
Placebo N = 86 |
Xanomeline Low Dose N = 84 |
Xanomeline High Dose N = 84 |
|---|---|---|---|
| Age | |||
| Median (IQR) | 76 (69, 82) | 78 (71, 82) | 76 (71, 80) |
| Mean (SD) | 75 (9) | 76 (8) | 74 (8) |
| Range | 52 - 89 | 51 - 88 | 56 - 88 |
| Age Group, n (%) | |||
| <65 | 14 (16%) | 8 (10%) | 11 (13%) |
| 65-80 | 42 (49%) | 47 (56%) | 55 (65%) |
| >80 | 30 (35%) | 29 (35%) | 18 (21%) |
| Female, n (%) | 53 (62%) | 50 (60%) | 40 (48%) |
The table above has three types of data summaries: a
continuous variable summary for AGE, a
categorical variable summary for AGEGR1,
and a dichotomous variable summary for
SEX.
Continuous Summaries
To get a continuous variable summary, we will use the
ard_continuous() function from the {cards}
package.
df_continuous_ard <-
ard_continuous(
ADSL,
by = ARM,
variables = AGE,
statistic = ~ continuous_summary_fns(c("median", "p25", "p75", "mean", "sd", "min", "max"))
)
df_continuous_ard |> head(5)
#> {cards} data frame: 5 x 10
#> group1 group1_level variable stat_name stat_label stat
#> 1 ARM Placebo AGE median Median 76
#> 2 ARM Placebo AGE p25 25th Per<U+2026> 69
#> 3 ARM Placebo AGE p75 75th Per<U+2026> 82
#> 4 ARM Placebo AGE mean Mean 75.209
#> 5 ARM Placebo AGE sd SD 8.59
#> i 4 more variables: context, fmt_fn, warning, errorCategorical Summaries
To get the categorical variable summary, we will use the
ard_categorical() function.
df_categorical_ard <-
ard_categorical(
ADSL,
by = ARM,
variables = AGEGR1
)
df_categorical_ard |> head(5)
#> {cards} data frame: 5 x 11
#> group1 group1_level variable variable_level stat_name stat_label stat
#> 1 ARM Placebo AGEGR1 65-80 n n 42
#> 2 ARM Placebo AGEGR1 65-80 N N 86
#> 3 ARM Placebo AGEGR1 65-80 p % 0.488
#> 4 ARM Xanomeli<U+2026> AGEGR1 65-80 n n 55
#> 5 ARM Xanomeli<U+2026> AGEGR1 65-80 N N 84
#> i 4 more variables: context, fmt_fn, warning, errorDichotomous Summaries
To get the dichotomous variable summary, we will use
ard_dichotomous(). In this case, we want to show the Female
("F") level of the SEX variable and specify
this with the values argument.
df_dichotomous_ard <-
ard_dichotomous(
ADSL,
by = ARM,
variables = SEX,
value = list(SEX = "F")
)
df_dichotomous_ard |> head(5)
#> {cards} data frame: 5 x 11
#> group1 group1_level variable variable_level stat_name stat_label stat
#> 1 ARM Placebo SEX F n n 53
#> 2 ARM Placebo SEX F N N 86
#> 3 ARM Placebo SEX F p % 0.616
#> 4 ARM Xanomeli<U+2026> SEX F n n 40
#> 5 ARM Xanomeli<U+2026> SEX F N N 84
#> i 4 more variables: context, fmt_fn, warning, errorCombine Results
As a last step, you can combine all of these objects into a single
object using bind_ard(), which is similar to
dplyr::bind_rows() and includes additional structural
checks for our results.
bind_ard(
df_continuous_ard,
df_categorical_ard,
df_dichotomous_ard
)
#> {cards} data frame: 57 x 11
#> group1 group1_level variable variable_level stat_name stat_label
#> 1 ARM Placebo AGE median Median
#> 2 ARM Placebo AGE p25 25th Per<U+2026>
#> 3 ARM Placebo AGE p75 75th Per<U+2026>
#> 4 ARM Placebo AGE mean Mean
#> 5 ARM Placebo AGE sd SD
#> 6 ARM Placebo AGE min Min
#> 7 ARM Placebo AGE max Max
#> 8 ARM Xanomeli<U+2026> AGE median Median
#> 9 ARM Xanomeli<U+2026> AGE p25 25th Per<U+2026>
#> 10 ARM Xanomeli<U+2026> AGE p75 75th Per<U+2026>
#> stat
#> 1 76
#> 2 69
#> 3 82
#> 4 75.209
#> 5 8.59
#> 6 52
#> 7 89
#> 8 76
#> 9 70.5
#> 10 80
#> i 47 more rows
#> i Use `print(n = ...)` to see more rows
#> i 4 more variables: context, fmt_fn, warning, errorShortcut
The ard_stack() function provides a shortcut to perform
the calculations above in a single step.
In the example below, the data and by
arguments are passed to each subsequent ard_*() function
call. Moreover, we will also be returned the univariate tabulation of
the by variable, which would be used to add counts to the
header row of the table.
ard_stack(
data = ADSL,
.by = ARM,
ard_continuous(
variables = AGE,
statistic = ~ continuous_summary_fns(c("median", "p25", "p75", "mean", "sd", "min", "max"))
),
ard_categorical(variables = AGEGR1),
ard_dichotomous(variables = SEX, value = list(SEX = "F"))
)
#> {cards} data frame: 66 x 11
#> group1 group1_level variable variable_level stat_name stat_label
#> 1 ARM Placebo AGE median Median
#> 2 ARM Placebo AGE p25 25th Per<U+2026>
#> 3 ARM Placebo AGE p75 75th Per<U+2026>
#> 4 ARM Placebo AGE mean Mean
#> 5 ARM Placebo AGE sd SD
#> 6 ARM Placebo AGE min Min
#> 7 ARM Placebo AGE max Max
#> 8 ARM Xanomeli<U+2026> AGE median Median
#> 9 ARM Xanomeli<U+2026> AGE p25 25th Per<U+2026>
#> 10 ARM Xanomeli<U+2026> AGE p75 75th Per<U+2026>
#> stat
#> 1 76
#> 2 69
#> 3 82
#> 4 75.209
#> 5 8.59
#> 6 52
#> 7 89
#> 8 76
#> 9 70.5
#> 10 80
#> i 56 more rows
#> i Use `print(n = ...)` to see more rows
#> i 4 more variables: context, fmt_fn, warning, error