# Introduction to tern

#### 2022-03-09

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.

    library(tern)
    library(dplyr)

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 used is `analyze_vars` which replaces the `rtables::analyze` function used 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 Trial 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 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.

    library(nestcolor)

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.

## Interactive Apps

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.

## Summary

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.