Code
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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).
Output
The user can select different plotting_choices depending on their preference. To demonstrate, separate plots are produced with a maximum of 3 observations each.
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]]
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$USUBJID)
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adlb <- df_explicit_na(adlb)
adlb_f <- adlb %>%
filter(
SAFFL == "Y",
PARAMCD == "ALT",
AVISIT != "SCREENING",
ARMCD == "ARM A"
) %>%
mutate(Patient_ID = sub(".*id-", "", USUBJID))
teal App
Code
library(teal.modules.clinical)
## Data reproducible code
data <- teal_data()
data <- within(data, {
library(dplyr)
# use small sample size
ADSL <- random.cdisc.data::cadsl %>% slice(1:15)
ADLB <- random.cdisc.data::cadlb %>% filter(USUBJID %in% ADSL$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) <- datanamesWarning: `datanames<-()` was deprecated in teal.data 0.6.1.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
`teal_data`. See ?names.teal_dataCode
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.
#| '!! 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://pharmaverse.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] "2024-11-20 17:41:22 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 2024-11-20
pandoc 3.4 @ /usr/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
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