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.
rtables
Analytical 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.
Defining the table layout with a pure rtables code:
Below, the only tern function used is analyze_vars which replaces the rtables::analyze function used above.
# 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
#> Min - Max 1.0 - 4.0 1.0 - 4.0 1.0 - 4.0We see that tern offers advanced analysis by extending rtables function calls with only one additional function call.
More examples with tabulation analyze functions are presented in the Tabulation vignette.
Clinical trial related plots complement the rich palette of tern tabulation analysis functions. Thus the tern package delivers a full-featured tool for clinical trial reporting. The tern plot functions return graphs as ggplot2 objects.
adsl <- formatters::ex_adsl
adlb <- formatters::ex_adlb
adlb <- dplyr::filter(adlb, PARAMCD == "ALT", AVISIT != "SCREENING")The nestcolor package can be loaded in to apply the standardized NEST color palette to all tern plots.
Line plot without a table generated by the g_lineplot function.
# Mean with CI
g_lineplot(adlb, adsl, subtitle = "Laboratory Test:")Line plot with a table generated by the g_lineplot function.
# Mean with CI, table, and customized confidence level
g_lineplot(
adlb,
adsl,
table = c("n", "mean", "mean_ci"),
title = "Plot of Mean and 80% Confidence Limits by Visit"
)All tern functions used for plot generation are g_ prefixed and are listed on the tern website functions reference.
Most tern outputs can be easily converted into shiny apps. We recommend building apps using the teal package, a shiny-based interactive exploration framework for analyzing data. A variety of pre-made teal shiny apps for tern outputs are available in the teal.modules.clinical package.
In summary, tern contains many additional functions for creating tables, listings, and graphs used in clinical trials and other statistical analyses. The design of the package gives users the flexibility to meet the analysis needs in both regulatory and exploratory reporting contexts.
For more information please explore the tern website.
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