teal applications

teal is a Shiny-based framework that streamlines the process of creating an application for clinical trials data. App developers are required to specify input data as well as analysis modules choosing from a wide range of available modules or creating their own module.

The main features of a teal app include:

• filter panel allowing to filtering input data for e.g. subgroup analysis
• reproducibility code for each output
• reporter to export the results
• and more…

Please visit teal package webpage to see more. Please also see teal.gallery for more examples.

Below you can find a few examples using WebR (via Shinylive). This will run applications directly in the browser without any server in the background. You are free to change the source code and the application will refresh accordingly.

A simple example for non-clinical trials data with custom module

#| standalone: true
#| viewerHeight: 800
#| components: [viewer, editor]
#| layout: vertical
library(teal)

app <- init(
  data = teal_data(iris = iris),
  modules = list(
    module(
      label = "iris histogram",
      server = function(input, output, session, data) {
        updateSelectInput(session = session,
                          inputId =  "var",
                          choices = names(data()[["iris"]])[1:4])

        output$hist <- renderPlot({
          req(input$var)
          hist(
            x = data()[["iris"]][[input$var]],
            main = sprintf("Histogram of %s", input$var),
            xlab = input$var
          )
        })
      },
      ui = function(id) {
        ns <- NS(id)
        list(
          selectInput(inputId = ns("var"),
                      label =  "Column name",
                      choices = NULL),
          plotOutput(outputId = ns("hist"))
        )
      }
    )
  )
)

shinyApp(app$ui, app$server)

A simple example for clinical trials data with custom module

#| standalone: true
#| viewerHeight: 800
#| components: [viewer, editor]
#| layout: vertical
library(teal)
library(tern)
library(dplyr)

adsl <- tern_ex_adsl %>%
  df_explicit_na()

app <- init(
  data = cdisc_data(
    adsl = adsl
  ),
  modules = list(
    module(
      "demographic table",
      server = function(input, output, session, data) {
        output$table <- renderUI({
          validate(need(input$vars, "Please select summary variables"))

          lyt <- basic_table(show_colcounts = TRUE) %>%
            split_cols_by(var = "ACTARM") %>%
            add_overall_col("All Patients") %>%
            analyze_vars(
                vars = input$vars,
                var_labels = var_labels(data()[["adsl"]][, input$vars])
            )

            result <- build_table(lyt, adsl)

            as_html(result)
        })
      },
      ui = function(id) {
        ns <- NS(id)
        list(
          shiny::selectInput(
            ns("vars"),
            "Summary variables",
            choices = c("AGE", "SEX", "RACE", "STRATA1", "STRATA2", "BMRKR1", "BMRKR2"),
            selected = c("AGE", "SEX", "RACE"),
            multiple = TRUE
          ),
          uiOutput(ns("table"))
        )
      }
    )
  )
)

shinyApp(app$ui, app$server)

Use pre-created modules

#| standalone: true
#| viewerHeight: 800
#| components: [viewer, editor]
#| layout: vertical
library(teal.modules.clinical)
library(teal.modules.general)
library(sparkline)
library(magrittr)

data <- cdisc_data() %>%
  within({
    ADSL <- tmc_ex_adsl
    ADTTE <- tmc_ex_adtte
  })
datanames(data) <- c("ADSL", "ADTTE")
join_keys(data) <- default_cdisc_join_keys[datanames(data)]

ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]

arm_vars <- c("ARM", "ARMCD", "ACTARMCD")
strata_vars <- c("STRATA1", "STRATA2")
facet_vars <- c("BMRKR2", "SEX", "COUNTRY")

cs_arm_var <- choices_selected(
  choices = variable_choices(ADSL, subset = arm_vars),
  selected = "ARM"
)

cs_strata_var <- choices_selected(
  choices = variable_choices(ADSL, subset = strata_vars),
  selected = "STRATA1"
)

cs_facet_var <- choices_selected(
  choices = variable_choices(ADSL, subset = facet_vars),
  selected = "BMRKR2"
)

cs_paramcd_tte <- choices_selected(
  choices = value_choices(ADTTE, "PARAMCD", "PARAM"),
  selected = "OS"
)

date_vars_asl <- names(ADSL)[vapply(ADSL, function(x) inherits(x, c("Date", "POSIXct", "POSIXlt")), logical(1))]
demog_vars_asl <- names(ADSL)[!(names(ADSL) %in% c("USUBJID", "STUDYID", date_vars_asl))]

arm_ref_comp <- list(
  ACTARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  ),
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  )
)


app <- init(
  data = data,
  modules = list(
    tm_data_table("Data Table"),
    tm_variable_browser("Variable Browser"),
    tm_t_summary(
      label = "Demographic Table",
      dataname = "ADSL",
      arm_var = cs_arm_var,
      summarize_vars = choices_selected(
        choices = variable_choices(ADSL, demog_vars_asl),
        selected = c("SEX", "AGE", "RACE")
      )
    ),
    tm_g_km(
      label = "Kaplan Meier Plot",
      dataname = "ADTTE",
      arm_var = cs_arm_var,
      arm_ref_comp = arm_ref_comp,
      paramcd = cs_paramcd_tte,
      facet_var = cs_facet_var,
      strata_var = cs_strata_var,
      plot_height = c(1800L, 200L, 4000L)
    ),
    tm_t_tte(
      label = "Time To Event Table",
      dataname = "ADTTE",
      arm_var = cs_arm_var,
      arm_ref_comp = arm_ref_comp,
      paramcd = cs_paramcd_tte,
      strata_var = cs_strata_var,
      time_points = choices_selected(c(182, 243), 182),
      event_desc_var = choices_selected(
        variable_choices(ADTTE, "EVNTDESC"),
        "EVNTDESC",
        fixed = TRUE
      )
    )
  )
)

shinyApp(app$ui, app$server)