## Warning: package 'r2rtf' was built under R version 4.4.3There are 2 key metadata types:
The code below builds the subject-level metadata directly from the example dataset.
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()The AE metadata is built directly from the example subject-level and adverse event datasets.
adsl_ae <- metalite_sl_adsl
adsl_ae$RACE <- tools::toTitleCase(adsl_ae$RACE)
adae <- metalite_sl_adae
analysis_plan <- plan(
analysis = "ae_specific",
population = "apat",
observation = "wk12",
parameter = "rel;ser"
)
meta_ae <- meta_adam(observation = adae, population = adsl_ae) |>
define_plan(analysis_plan) |>
define_population(
name = "apat",
var = c(
"USUBJID", "SAFFL", "TRTA", "TRTDUR",
"SITEID", "SEX", "RACE", "AGE"
),
group = "TRTA",
subset = SAFFL == "Y",
label = "All Participants as Treated"
) |>
define_observation(
name = "wk12",
var = c(
"USUBJID", "SAFFL", "TRTA", "SEX", "AEDECOD", "AEBODSYS", "AEREL",
"AESER", "AEOUT", "AEACN", "AESDTH", "ASTDT", "AENDT"
),
group = "TRTA",
subset = SAFFL == "Y",
label = "Weeks 0 to 12"
) |>
define_parameter(
name = "rel",
term1 = "Drug-Related",
term2 = "",
subset = AEREL %in% c("POSSIBLE", "PROBABLE"),
var = "AEDECOD",
soc = "AEBODSYS",
label = "Drug-related AEs"
) |>
define_parameter(
name = "ser",
term1 = "Serious",
term2 = "",
subset = AESER == "Y",
var = "AEDECOD",
soc = "AEBODSYS",
label = "Serious AEs"
) |>
define_analysis(
name = "ae_specific",
title = "Participants With Drug-Related Adverse Events"
) |>
meta_build()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