Create a clinical reporting table with tern/rtables
tern_summary_table.Rdtern_summary_table() is a convenience wrapper around {rtables} and
{tern} commands to generate a clinical reporting summary statistics tables
whilst using a similar interface as summary_table(). This can be helpful
for side by side comparisons of the two functions.
Arguments
- dt
A
data.framecontaining, at least, the variables indicated intargetandtreat.- target
Target variable passed as a string for which summary statistics are to be calculated.
- treat
A string indicating the grouping variable, e.g. the variable specifying the treatment population.
- target_name
Heading for the target variable as a string. Defaults to
target.
Value
A data.table of summary statistics. The format depends on the
type of the target variable:
If the
targetvariable is categorical, i.e. typecharacter,factororlogicalthen the output is that ofcalc_counts().If the
targetvariable is numeric, then the output is that ofcalc_desc().
Examples
dmg_vars <- c("AGE", "RACE", "ETHNIC")
dmg_var_lbls <- c("Age (yr)", "Race", "Ethnicity")
# Demographics table (DMT01)
tern_summary_table(
adsl,
target = dmg_vars,
treat = 'ARM',
target_name = dmg_var_lbls
)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 134 134 132
#> Mean (SD) 33.8 (6.6) 35.4 (7.9) 35.4 (7.7)
#> Median 33.0 35.0 35.0
#> Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0
#> Race
#> n 134 134 132
#> ASIAN 68 (50.7%) 67 (50%) 73 (55.3%)
#> BLACK OR AFRICAN AMERICAN 31 (23.1%) 28 (20.9%) 32 (24.2%)
#> WHITE 27 (20.1%) 26 (19.4%) 21 (15.9%)
#> AMERICAN INDIAN OR ALASKA NATIVE 8 (6%) 11 (8.2%) 6 (4.5%)
#> MULTIPLE 0 1 (0.7%) 0
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 (0.7%) 0
#> OTHER 0 0 0
#> UNKNOWN 0 0 0
#> Ethnicity
#> n 134 134 132
#> HISPANIC OR LATINO 15 (11.2%) 18 (13.4%) 15 (11.4%)
#> NOT HISPANIC OR LATINO 104 (77.6%) 103 (76.9%) 101 (76.5%)
#> NOT REPORTED 6 (4.5%) 10 (7.5%) 11 (8.3%)
#> UNKNOWN 9 (6.7%) 3 (2.2%) 5 (3.8%)
# Demographics table (DMT01) with continuous variable (e.g., BMRKR1)
tern_summary_table(
adsl,
target = c(dmg_vars, "BMRKR1"),
treat = 'ARM',
target_name = c(dmg_var_lbls, "Biomarker 1")
)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 134 134 132
#> Mean (SD) 33.8 (6.6) 35.4 (7.9) 35.4 (7.7)
#> Median 33.0 35.0 35.0
#> Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0
#> Race
#> n 134 134 132
#> ASIAN 68 (50.7%) 67 (50%) 73 (55.3%)
#> BLACK OR AFRICAN AMERICAN 31 (23.1%) 28 (20.9%) 32 (24.2%)
#> WHITE 27 (20.1%) 26 (19.4%) 21 (15.9%)
#> AMERICAN INDIAN OR ALASKA NATIVE 8 (6%) 11 (8.2%) 6 (4.5%)
#> MULTIPLE 0 1 (0.7%) 0
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 (0.7%) 0
#> OTHER 0 0 0
#> UNKNOWN 0 0 0
#> Ethnicity
#> n 134 134 132
#> HISPANIC OR LATINO 15 (11.2%) 18 (13.4%) 15 (11.4%)
#> NOT HISPANIC OR LATINO 104 (77.6%) 103 (76.9%) 101 (76.5%)
#> NOT REPORTED 6 (4.5%) 10 (7.5%) 11 (8.3%)
#> UNKNOWN 9 (6.7%) 3 (2.2%) 5 (3.8%)
#> Biomarker 1
#> n 134 134 132
#> Mean (SD) 6.0 (3.6) 5.7 (3.3) 5.6 (3.5)
#> Median 5.4 4.8 4.6
#> Min - Max 0.4 - 17.7 0.6 - 14.2 0.2 - 21.4