IPPG01

Individual Patient Plot Over Time

For illustration purposes, we will subset the adlb dataset for safety population in treatment arm A and a specific lab parameter (ALT).

Preview
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The user can select different plotting_choices depending on their preference. To demonstrate, separate plots are produced with a maximum of 3 observations each.

Code

plots <- g_ipp(
  df = adlb_f,
  xvar = "AVISIT",
  yvar = "AVAL",
  xlab = "Visit",
  ylab = "SGOT/ALT (U/L)",
  id_var = "Patient_ID",
  title = "Individual Patient Plots",
  subtitle = "Treatment Arm A",
  plotting_choices = "split_by_max_obs",
  max_obs_per_plot = 3
)
plots

[[1]]


[[2]]

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.

Preview
Try this using WebR

Here, patients’ individual baseline values will be shown for reference. Note that users can provide their own custom theme to the function via the ggtheme argument.

Code

plots <- g_ipp(
  df = adlb_f,
  xvar = "AVISIT",
  yvar = "AVAL",
  xlab = "Visit",
  ylab = "SGOT/ALT (U/L)",
  id_var = "Patient_ID",
  title = "Individual Patient Plots",
  subtitle = "Treatment Arm A",
  add_baseline_hline = TRUE,
  yvar_baseline = "BASE",
  ggtheme = theme_minimal(),
  plotting_choices = "split_by_max_obs",
  max_obs_per_plot = 3
)
plots

[[1]]


[[2]]

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.

Code

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

# use small sample size
adsl <- random.cdisc.data::cadsl %>% slice(1:15)
adlb <- random.cdisc.data::cadlb %>% filter(USUBJID %in% adsl$USUBJID)

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adlb <- df_explicit_na(adlb)

adlb_f <- adlb %>%
  filter(
    SAFFL == "Y",
    PARAMCD == "ALT",
    AVISIT != "SCREENING",
    ARMCD == "ARM A"
  ) %>%
  mutate(Patient_ID = sub(".*id-", "", USUBJID))

Code

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  # use small sample size
  ADSL <- random.cdisc.data::cadsl %>% slice(1:15)
  ADLB <- random.cdisc.data::cadlb %>% filter(USUBJID %in% ADSL$USUBJID)

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADLB <- df_explicit_na(ADLB) %>%
    filter(AVISIT != "SCREENING")
})
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_ipp(
      label = "Individual Patient Plot",
      dataname = "ADLB",
      arm_var = choices_selected(
        value_choices(ADLB, c("ARMCD")),
        "ARM A"
      ),
      paramcd = choices_selected(
        value_choices(ADLB, "PARAMCD"),
        "ALT"
      ),
      aval_var = choices_selected(
        variable_choices(ADLB, c("AVAL")),
        "AVAL"
      ),
      avalu_var = choices_selected(
        variable_choices(ADLB, c("AVALU")),
        "AVALU",
        fixed = TRUE
      ),
      id_var = choices_selected(
        variable_choices(ADLB, c("USUBJID")),
        "USUBJID",
        fixed = TRUE
      ),
      visit_var = choices_selected(
        variable_choices(ADLB, c("AVISIT")),
        "AVISIT"
      ),
      baseline_var = choices_selected(
        variable_choices(ADLB, c("BASE")),
        "BASE",
        fixed = TRUE
      ),
      add_baseline_hline = FALSE,
      separate_by_obs = FALSE
    )
  )
)

shinyApp(app$ui, app$server)

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.

#| 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, {
  library(dplyr)

  # use small sample size
  ADSL <- random.cdisc.data::cadsl %>% slice(1:15)
  ADLB <- random.cdisc.data::cadlb %>% filter(USUBJID %in% ADSL$USUBJID)

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADLB <- df_explicit_na(ADLB) %>%
    filter(AVISIT != "SCREENING")
})
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_ipp(
      label = "Individual Patient Plot",
      dataname = "ADLB",
      arm_var = choices_selected(
        value_choices(ADLB, c("ARMCD")),
        "ARM A"
      ),
      paramcd = choices_selected(
        value_choices(ADLB, "PARAMCD"),
        "ALT"
      ),
      aval_var = choices_selected(
        variable_choices(ADLB, c("AVAL")),
        "AVAL"
      ),
      avalu_var = choices_selected(
        variable_choices(ADLB, c("AVALU")),
        "AVALU",
        fixed = TRUE
      ),
      id_var = choices_selected(
        variable_choices(ADLB, c("USUBJID")),
        "USUBJID",
        fixed = TRUE
      ),
      visit_var = choices_selected(
        variable_choices(ADLB, c("AVISIT")),
        "AVISIT"
      ),
      baseline_var = choices_selected(
        variable_choices(ADLB, c("BASE")),
        "BASE",
        fixed = TRUE
      ),
      add_baseline_hline = FALSE,
      separate_by_obs = FALSE
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 17:54:49 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
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 rmarkdown               2.28     2024-08-17 [1] CRAN (R 4.4.1)
 rtables               * 0.6.10   2024-09-20 [1] CRAN (R 4.4.1)
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Reproducibility

Timestamp

Session Info

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`teal` App

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