The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Build an Interactive Baseline Characteristic Table

library(r2rtf)
library(metalite)
library(metalite.ae)
library(metalite.sl)

There are 2 key metadata types:

Build metadata

Metadata for baseline characteristic table

The code below is the same as meta_sl_example().

adsl <- r2rtf::r2rtf_adsl
adsl$TRTA <- adsl$TRT01A
adsl$TRTA <- factor(adsl$TRTA,
  levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"),
  labels = c("Placebo", "Low Dose", "High Dose")
)

meta_sl <- meta_adam(
  population = adsl,
  observation = adsl
) |>
  define_plan(plan = plan(
    analysis = "base_char", population = "apat",
    observation = "apat", parameter = "age;gender;race"
  )) |>
  define_population(
    name = "apat",
    group = "TRTA",
    subset = quote(SAFFL == "Y"),
    var = c("USUBJID", "TRTA", "SAFFL", "AGEGR1", "SEX", "RACE")
  ) |>
  define_observation(
    name = "apat",
    group = "TRTA",
    subset = quote(SAFFL == "Y"),
    var = c("USUBJID", "TRTA", "SAFFL", "AGEGR1", "SEX", "RACE")
  ) |>
  define_parameter(
    name = "age",
    var = "AGE",
    label = "Age (years)",
    vargroup = "AGEGR1"
  ) |>
  define_parameter(
    name = "gender",
    var = "SEX",
    label = "Gender"
  ) |>
  define_parameter(
    name = "race",
    var = "RACE",
    label = "Race"
  ) |>
  define_analysis(
    name = "base_char",
    title = "Participant Baseline Characteristics by Treatment Group",
    label = "baseline characteristic table"
  ) |>
  meta_build()

A metadata of the AE subgroup specific analysis

In this vignette, we will directly use the metadata built by meta_ae_example().

meta_ae <- meta_ae_example()

Customization (Optional)

If you want to capitalize only the first letter of “RACE” (e.g., Black or african american) or any other character variable, you can customize the react_base_char function at the beginning of the code.

# function to capitalize the first letter of a string that has multiple words
capitalize_words <- function(x) {
  sapply(x, function(word) {
    paste0(toupper(substr(word, 1, 1)), tolower(substr(word, 2, nchar(word))))
  })
}
# 1) In "data_population": extract the RACE values as a character vector
race_values_pop <- meta_sl[["data_population"]]$RACE # Use $ to get a vector

# Capitalize the race values
meta_sl[["data_population"]]$RACE <- capitalize_words(race_values_pop) # Assign back as a vector

# 2) In "data_observation": extract the RACE values as a character vector
race_values_obs <- meta_sl[["data_observation"]]$RACE # Use $ to get a vector

# Capitalize the race values
meta_sl[["data_observation"]]$RACE <- capitalize_words(race_values_obs) # Assign back as a vector

Build a reactable

Baseline characteristic table + Participants With Serious AE

react_base_char(
  metadata_sl = meta_sl,
  metadata_ae = meta_ae,
  ae_subgroup = c("age", "race", "gender"),
  ae_specific = "ser",
  width = 1200
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.