## 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.

## 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
`tern::count_occurrences`

,
`tern::summarize_ancova`

and
`tern::summarize_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
`summarize_vars`

which replaces the
`rtables::analyze`

function above.

```
# Create table layout with tern summarize_vars analyze function
lyt2 <- rtables::basic_table() %>%
rtables::split_cols_by(var = "ARM") %>%
rtables::split_rows_by(var = "AVISIT") %>%
tern::summarize_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
`tern::g_lineplot`

function.

```
# Mean with CI
tern::g_lineplot(adlb, adsl, subtitle = "Laboratory Test:")
```

Line plot with a table generated by the `tern::g_lineplot`

function.

```
# Mean with CI, table and customized confidence level
tern::g_lineplot(
adlb,
adsl,
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.

## Summary

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.**