A: Drug X B: Placebo C: Combination All Patients
Analysis Visit (N=134) (N=134) (N=132) (N=400)
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BASELINE
n 134 134 132 400
Mean (SD) 17.7 (9.9) 18.7 (9.8) 19.5 (9.1) 18.6 (9.6)
Median 17.5 18.2 19.0 18.0
Min - Max 0.0 - 44.1 1.5 - 54.4 0.6 - 39.8 0.0 - 54.4
WEEK 1 DAY 8
n 134 134 132 400
Mean (SD) 16.8 (9.1) 18.9 (9.2) 19.6 (9.3) 18.4 (9.2)
Median 16.0 18.2 19.0 18.0
Min - Max 0.1 - 36.3 0.7 - 39.9 0.9 - 44.7 0.1 - 44.7
WEEK 2 DAY 15
n 134 134 132 400
Mean (SD) 17.8 (9.6) 18.8 (9.7) 16.5 (8.2) 17.7 (9.2)
Median 15.9 18.0 17.0 17.0
Min - Max 0.4 - 44.3 0.2 - 44.3 0.3 - 34.7 0.2 - 44.3
WEEK 3 DAY 22
n 134 134 132 400
Mean (SD) 18.4 (9.3) 17.6 (9.6) 16.8 (9.5) 17.6 (9.5)
Median 18.1 17.7 15.1 17.2
Min - Max 0.6 - 41.7 0.0 - 38.6 0.5 - 39.2 0.0 - 41.7
WEEK 4 DAY 29
n 134 134 132 400
Mean (SD) 19.2 (11.0) 17.2 (10.6) 17.9 (9.3) 18.1 (10.3)
Median 17.4 15.9 17.7 17.3
Min - Max 0.9 - 54.2 0.4 - 48.0 0.2 - 41.3 0.2 - 54.2
WEEK 5 DAY 36
n 134 134 132 400
Mean (SD) 19.2 (9.5) 18.0 (9.9) 18.5 (9.4) 18.6 (9.6)
Median 19.8 18.3 19.3 19.0
Min - Max 0.0 - 43.4 0.1 - 40.6 0.0 - 37.5 0.0 - 43.4
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
# Preparation of an illustrative datasetlibrary(tern)adsl <- random.cdisc.data::cadsladlb <- random.cdisc.data::cadlbadlb_labels <-var_labels(adlb)# For illustration purposes, the example focuses on ALT# Measurements starting from baseline and excluding all screening visitsadlb <-subset(adlb, AVISIT !="SCREENING"& PARAMCD =="ALT")adlb$AVISIT <-droplevels(adlb$AVISIT)var_labels(adlb) <- adlb_labels# 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)
library(teal.modules.clinical)## Data reproducible codedata <-teal_data()data <-within(data, { ADSL <- random.cdisc.data::cadsl ADLB <- random.cdisc.data::cadlb# 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)})datanames <-c("ADSL", "ADLB")datanames(data) <- datanamesjoin_keys(data) <- default_cdisc_join_keys[datanames]## Reusable Configuration For ModulesADSL <- data[["ADSL"]]ADLB <- data[["ADLB"]]## Setup Appapp <-init(data = data,modules =modules(tm_t_summary_by(label ="Laboratory Test Results by Visit",dataname ="ADLB",arm_var =choices_selected(choices =variable_choices(ADSL, c("ARM", "ARMCD")),selected ="ARM" ),by_vars =choices_selected(# note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`choices =variable_choices(ADLB, c("PARAM", "AVISIT")),selected =c("PARAM", "AVISIT") ),summarize_vars =choices_selected(choices =variable_choices(ADLB, c("AVAL")),selected =c("AVAL") ),useNA ="ifany",paramcd =choices_selected(choices =value_choices(ADLB, "PARAMCD", "PARAM"),selected ="ALT" ) ) ),filter =teal_slices(teal_slice("ADLB", "AVAL", selected =NULL)))shinyApp(app$ui, app$server)
Warning: The 'plotly_relayout' event tied a source ID of
'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_AVAL-inputs-histogram_plot'
is not registered. In order to obtain this event data, please add
`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to
obtain event data from.
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 codedata <- teal_data()data <- within(data, { ADSL <- random.cdisc.data::cadsl ADLB <- random.cdisc.data::cadlb # 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)})datanames <- c("ADSL", "ADLB")datanames(data) <- datanamesjoin_keys(data) <- default_cdisc_join_keys[datanames]## Reusable Configuration For ModulesADSL <- data[["ADSL"]]ADLB <- data[["ADLB"]]## Setup Appapp <- init( data = data, modules = modules( tm_t_summary_by( label = "Laboratory Test Results by Visit", dataname = "ADLB", arm_var = choices_selected( choices = variable_choices(ADSL, c("ARM", "ARMCD")), selected = "ARM" ), by_vars = choices_selected( # note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT` choices = variable_choices(ADLB, c("PARAM", "AVISIT")), selected = c("PARAM", "AVISIT") ), summarize_vars = choices_selected( choices = variable_choices(ADLB, c("AVAL")), selected = c("AVAL") ), useNA = "ifany", paramcd = choices_selected( choices = value_choices(ADLB, "PARAMCD", "PARAM"), selected = "ALT" ) ) ), filter = teal_slices(teal_slice("ADLB", "AVAL", selected = NULL)))shinyApp(app$ui, app$server)
