This vignette shows the general purpose and syntax of the
tern R package. The tern R package contains
analytical functions for creating tables and graphs useful for clinical
trials and other statistical analysis. The main focus is on the clinical
trial reporting tables but the graphs related to the clinical trials are
also valuable. The core functionality for tabulation is built on top of
the more general purpose rtables package.
The package provides a large range of functionality to create tables and graphs used for clinical trial and other statistical analysis.
rtables tabulation extended by clinical trials specific
functions:
rtables tabulation helper functions:
data visualizations connected with clinical trials:
data visualizations helper functions:
The reference of tern functions is available on the
tern website functions reference.
rtablesAnalytical functions are used in combination with other
rtables layout functions, in the pipeline which creates the
rtables table. They apply some statistical logic to the
layout of the rtables table. The table layout is
materialized with the rtables::build_table function and the
data.
The tern analytical functions are wrappers around the
rtables::analyze function; they offer various methods
useful from the perspective of clinical trials and other statistical
projects.
Examples of the tern analytical functions are
count_occurrences, summarize_ancova and
analyze_vars. As there is no one prefix to identify all
tern analytical functions it is recommended to use the
reference subsection on the
tern website.
In the rtables code below we first describe the two
tables and assign the descriptions to the variables lyt and
lyt2. We then built the tables using the actual data with
rtables::build_table. The description of a table is called
a table layout. The analyze
instruction adds to the layout that the ARM
variable should be analyzed with the mean analysis function
and the result should be rounded to 1 decimal place. Hence, a
layout is “pre-data”; that is, it’s a description of
how to build a table once we get data.
tern.R
Defining the table layout with a pure rtables code.
# Create table layout pure rtables
lyt <- rtables::basic_table() %>%
rtables::split_cols_by(var = "ARM") %>%
rtables::split_rows_by(var = "AVISIT") %>%
rtables::analyze(vars = "AVAL", mean, format = "xx.x")tern.R
Below the only tern function is
analyze_vars which replaces the
rtables::analyze function above.
# Create table layout with tern analyze_vars analyze function
lyt2 <- rtables::basic_table() %>%
rtables::split_cols_by(var = "ARM") %>%
rtables::split_rows_by(var = "AVISIT") %>%
analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)"))tern.R
# Apply table layout to data and produce `rtables` object
adrs <- formatters::ex_adrs
rtables::build_table(lyt, df = adrs)
#> A: Drug X B: Placebo C: Combination
#> ——————————————————————————————————————————————————————————
#> SCREENING
#> mean 3.0 3.0 3.0
#> BASELINE
#> mean 2.5 2.8 2.5
#> END OF INDUCTION
#> mean 1.7 2.1 1.6
#> FOLLOW UP
#> mean 2.2 2.9 2.0
rtables::build_table(lyt2, df = adrs)
#> A: Drug X B: Placebo C: Combination
#> ———————————————————————————————————————————————————————————————
#> SCREENING
#> n 154 178 144
#> Mean (SD) (3.00, 0.00) (3.00, 0.00) (3.00, 0.00)
#> Median 3.0 3.0 3.0
#> Min - Max 3.0 - 3.0 3.0 - 3.0 3.0 - 3.0
#> BASELINE
#> n 136 146 124
#> Mean (SD) (2.46, 0.88) (2.77, 1.00) (2.46, 1.08)
#> Median 3.0 3.0 3.0
#> Min - Max 1.0 - 4.0 1.0 - 5.0 1.0 - 5.0
#> END OF INDUCTION
#> n 218 205 217
#> Mean (SD) (1.75, 0.90) (2.14, 1.28) (1.65, 1.06)
#> Median 2.0 2.0 1.0
#> Min - Max 1.0 - 4.0 1.0 - 5.0 1.0 - 5.0
#> FOLLOW UP
#> n 164 153 167
#> Mean (SD) (2.23, 1.26) (2.89, 1.29) (1.97, 1.01)
#> Median 2.0 4.0 2.0 </