CIG01

Confidence Interval Plot

---

Output

Plot of Mean and 95% CIs for Mean

Preview

The function stat_mean_ci from the tern package can be used with default values to draw the 95% confidence interval around the mean.

plot <- ggplot(
  data = adlb,
  mapping = aes(
    x = ARMCD, y = AVAL, color = SEX,
    lty = SEX, shape = SEX
  )
) +
  stat_summary(
    fun.data = tern::stat_mean_ci,
    geom = "errorbar",
    width = 0.1,
    position = position_dodge(width = 0.5)
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    position = position_dodge(width = 0.5)
  ) +
  labs(
    title = "Confidence Interval Plot by Treatment Group",
    caption = "Mean and 95% CIs for mean are displayed.",
    x = "Treatment Group",
    y = paste0(adlb$PARAMCD[1], " (", adlb$AVALU[1], ")")
  )
plot

Try this using WebR

Experimental use!

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Plot of Confidence Interval Using a Different Stratification Variable

Preview

plot <- ggplot(
  data = adlb,
  mapping = aes(
    x = ARMCD, y = AVAL, color = STRATA2,
    lty = STRATA2, shape = STRATA2
  )
) +
  stat_summary(
    fun.data = tern::stat_mean_ci,
    geom = "errorbar",
    width = 0.1,
    position = position_dodge(width = 0.5)
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    position = position_dodge(width = 0.5)
  ) +
  labs(
    title = "Confidence Interval Plot by Treatment Group",
    caption = "Mean and 95% CIs for mean are displayed.",
    x = "Treatment Group",
    y = paste0(adlb$PARAMCD[1], " (", adlb$AVALU[1], ")")
  )
plot

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Plot of Median and 95% CIs for Median

Preview

The function stat_median_ci from the tern package works similarly to stat_mean_ci.

plot <- ggplot(
  data = adlb,
  mapping = aes(
    x = ARMCD, y = AVAL, color = STRATA1,
    lty = STRATA1, shape = STRATA1
  )
) +
  stat_summary(
    fun.data = stat_median_ci,
    geom = "errorbar",
    width = 0.1,
    position = position_dodge(width = 0.5)
  ) +
  stat_summary(
    fun = median,
    geom = "point",
    position = position_dodge(width = 0.5)
  ) +
  labs(
    title = "Confidence Interval Plot by Treatment Group",
    caption = "Median and 95% CIs for median are displayed.",
    x = "Treatment Group",
    y = paste0(adlb$PARAMCD[1], " (", adlb$AVALU[1], ")")
  )
plot

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Plot of Median and 95% CIs for Median Using Different Alpha Level

Preview

To modify the confidence level for the estimation of the confidence interval, the call to stat_mean_ci (or stat_median_ci) can be slightly modified.

plot <- ggplot(
  data = adlb,
  mapping = aes(
    x = ARMCD, y = AVAL, color = SEX,
    lty = SEX, shape = SEX
  )
) +
  stat_summary(
    fun.data = function(x) tern::stat_mean_ci(x, conf_level = 0.9),
    geom = "errorbar",
    width = 0.1,
    position = position_dodge(width = 0.5)
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    position = position_dodge(width = 0.5)
  ) +
  labs(
    title = "Confidence Interval Plot by Treatment Group",
    caption = "Mean and 90% CIs for mean are displayed.",
    x = "Treatment Group",
    y = paste0(adlb$PARAMCD[1], " (", adlb$AVALU[1], ")")
  )
plot

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Table of Mean and Median

Preview

The corresponding table is simply obtained using the rtables framework:

lyt <- basic_table() %>%
  split_cols_by(var = "ARMCD") %>%
  analyze_vars(vars = "AVAL", .stats = c("mean_sd", "median"))
table <- build_table(lyt = lyt, df = adlb)
table

              ARM A        ARM B        ARM C   
————————————————————————————————————————————————
Mean (SD)   17.7 (9.9)   18.7 (9.8)   19.5 (9.1)
Median         17.5         18.2         19.0   

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Data Setup

library(tern)
library(ggplot2)
library(dplyr)
library(nestcolor)

adlb <- random.cdisc.data::cadlb %>%
  filter(PARAMCD == "ALT", AVISIT == "BASELINE")

teal App

Preview

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADLB <- random.cdisc.data::cadlb
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames

Warning: `datanames<-()` was deprecated in teal.data 0.7.0.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data

join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADLB <- data[["ADLB"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_ci(
      label = "Confidence Interval Plot",
      x_var = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = c("ARMCD", "BMRKR2"),
          selected = c("ARMCD"),
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      y_var = data_extract_spec(
        dataname = "ADLB",
        filter = list(
          filter_spec(
            vars = "PARAMCD",
            choices = levels(ADLB$PARAMCD),
            selected = levels(ADLB$PARAMCD)[1],
            multiple = FALSE,
            label = "Select lab:"
          ),
          filter_spec(
            vars = "AVISIT",
            choices = levels(ADLB$AVISIT),
            selected = levels(ADLB$AVISIT)[1],
            multiple = FALSE,
            label = "Select visit:"
          )
        ),
        select = select_spec(
          label = "Analyzed Value",
          choices = c("AVAL", "CHG"),
          selected = "AVAL",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      color = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Color by variable",
          choices = c("SEX", "STRATA1", "STRATA2"),
          selected = c("STRATA1"),
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  ),
  header = "Example of Confidence Interval Plot",
  footer = tags$p(
    class = "text-muted", "Source: `teal.modules.clinical::tm_g_ci`"
  )
)

Warning: The `header` argument of `init()` is deprecated as of teal 0.16.0.
ℹ Use `modify_header()` on the teal app object instead. See ?modify_header for
  examples and more details.

Warning: The `footer` argument of `init()` is deprecated as of teal 0.16.0.
ℹ Use `modify_footer()` on the teal app object instead. See ?modify_footer for
  examples and more details.

shinyApp(app$ui, app$server)

Try this using shinylive

Experimental use!

shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://insightsengineering.r-universe.dev", getOption("webr_pkg_repos")))

# -- APP CODE --
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADLB <- random.cdisc.data::cadlb
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADLB <- data[["ADLB"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_ci(
      label = "Confidence Interval Plot",
      x_var = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = c("ARMCD", "BMRKR2"),
          selected = c("ARMCD"),
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      y_var = data_extract_spec(
        dataname = "ADLB",
        filter = list(
          filter_spec(
            vars = "PARAMCD",
            choices = levels(ADLB$PARAMCD),
            selected = levels(ADLB$PARAMCD)[1],
            multiple = FALSE,
            label = "Select lab:"
          ),
          filter_spec(
            vars = "AVISIT",
            choices = levels(ADLB$AVISIT),
            selected = levels(ADLB$AVISIT)[1],
            multiple = FALSE,
            label = "Select visit:"
          )
        ),
        select = select_spec(
          label = "Analyzed Value",
          choices = c("AVAL", "CHG"),
          selected = "AVAL",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      color = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Color by variable",
          choices = c("SEX", "STRATA1", "STRATA2"),
          selected = c("STRATA1"),
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  ),
  header = "Example of Confidence Interval Plot",
  footer = tags$p(
    class = "text-muted", "Source: `teal.modules.clinical::tm_g_ci`"
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2025-07-05 18:02:21 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-07-05
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.7.32 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
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 MASS                    7.3-65   2025-02-28 [2] CRAN (R 4.5.0)
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 memoise                 2.0.1    2021-11-26 [1] RSPM
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 pillar                  1.11.0   2025-07-04 [1] RSPM
 pkgcache                2.2.4    2025-05-26 [1] RSPM
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 R6                      2.6.1    2025-02-15 [1] RSPM
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 shinyvalidate           0.1.3    2023-10-04 [1] RSPM
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 stringr                 1.5.1    2023-11-14 [1] RSPM
 survival                3.8-3    2024-12-17 [2] CRAN (R 4.5.0)
 systemfonts             1.2.3    2025-04-30 [1] RSPM
 teal                  * 0.16.0   2025-02-23 [1] RSPM
 teal.code             * 0.6.1    2025-02-14 [1] RSPM
 teal.data             * 0.7.0    2025-01-28 [1] RSPM
 teal.logger             0.3.2    2025-02-14 [1] RSPM
 teal.modules.clinical * 0.10.0   2025-02-28 [1] RSPM
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 teal.slice            * 0.6.0    2025-02-03 [1] RSPM
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 yaml                    2.3.10   2024-07-26 [1] RSPM
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 [3] /github/home/R/x86_64-pc-linux-gnu-library/4.5
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

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