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Introduction to tern

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.

It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables.

Common Clinical Trials Analyses

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:

  • demographics
  • unique patients
  • exposure across patients
  • change from baseline for parameters
  • statistical model fits: MMRM, logistic regression, Cox regression, …

rtables tabulation helper functions:

  • pre-processing
  • conversions and transformations

data visualizations connected with clinical trials:

  • Kaplan-Meier plots
  • forest plots
  • line plots

data visualizations helper functions:

  • arrange/stack multiple graphs
  • embellishing graphs/tables with metadata and details, such as adding titles, footnotes, page number, etc.

The reference of tern functions is available on the tern website functions reference.

Analytical Functions for 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.

# 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")

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)"))
# 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 - 4.0

We 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 Trials Visualizations

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 ggplot2 or gTree objects, the latter is returned when a table is attached to the plot.

adsl <- formatters::ex_adsl
adlb <- formatters::ex_adlb
adlb <- dplyr::filter(adlb, PARAMCD == "ALT", AVISIT != "SCREENING")

The optional 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
  table = c("n", "mean", "mean_ci"),
  title = "Plot of Mean and 80% Confidence Limits by Visit"

The first plot is a ggplot2 object and the second plot is a gTree object, as the latter contains the table. The second plot has to be properly resized to get a clear and readable table content.

The tern functions used for plot generation are mostly g_ prefixed. All tern plot functions are listed on the tern website functions reference.

Interactive Apps

Most of tern outputs could be easily accommodated into shiny apps. We recommend applying tern outputs into teal apps. The teal package is a shiny-based interactive exploration framework for analyzing data. teal shiny apps with tern outputs are available in the teal.modules.clinical package.


In summary, tern contains many additional functions for creating tables, listing and graphs used in clinical trials and other statistical analyses. The design of the package gives users a lot of flexibility to meet the analysis needs in a regulatory or exploratory reporting context.

For more information please explore the tern website.