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.

Introduction to metalite.sl

library(metalite)
library(metalite.sl)

Overview

metalite.sl R package designed for the analysis & reporting of subject-level analysis in clinical trials. It operates on ADaM datasets and adheres to the metalite structure. The package encompasses the following components:

Baseline Characteristics.

This R package offers a comprehensive software development lifecycle (SDLC) solution, encompassing activities such as definition, development, validation, and finalization of the analysis.

Highlighted features

Workflow

The overall workflow includes the following steps:

  1. Define metadata information using metalite R package.
  2. Prepare outdata using prepare_*() functions.
  3. Extend outdata using extend_*() functions (optional).
  4. Format outdata using format_*() functions.
  5. Create TLFs using tlf_*() functions.

For instance, we can illustrate the creation of a straightforward Baseline characteristic table as shown below.

sl_plan <- plan(
  analysis = "base_char",
  population = "apat",
  observation = "apat",
  parameter = "age;gender;race"
)

metadata_sl <- meta_adam(
  population = metalite_sl_adsl,
  observation = metalite_sl_adsl
) |>
  define_plan(sl_plan) |>
  define_population(
    name = "apat",
    group = "TRTA",
    subset = SAFFL == "Y"
  ) |>
  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"
  ) |>
  meta_build()
#> Warning in FUN(X[[i]], ...): base_char: has missing label
metadata_sl |>
  prepare_base_char(
    population = "apat",
    analysis = "base_char",
    parameter = "age;gender"
  ) |>
  format_base_char() |>
  rtf_base_char(
    source = "Source: [CDISCpilot: adam-adsl]",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )

An example for interactive baseline characteristic table:

analysis_plan <- plan(
  analysis = "ae_specific",
  population = "apat",
  observation = "wk12",
  parameter = "rel"
)

metadata_ae <- meta_adam(
  observation = metalite_sl_adae,
  population = metalite_sl_adsl
) |>
  define_plan(analysis_plan) |>
  define_population(
    name = "apat",
    group = "TRTA",
    subset = SAFFL == "Y",
    label = "All Participants as Treated"
  ) |>
  define_observation(
    name = "wk12",
    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_analysis(
    name = "ae_specific",
    title = "Participants With Drug-Related Adverse Events"
  ) |>
  meta_build()

react_base_char(
  metadata_sl = metadata_sl,
  metadata_ae = metadata_ae,
  population = "apat",
  observation = "wk12",
  display_total = TRUE,
  sl_parameter = "age;race",
  ae_subgroup = c("age", "race"),
  ae_specific = "rel",
  width = 1200
)

Additional examples and tutorials can be found on the package website, offering further guidance and illustrations.

Input

To implement the workflow in metalite.sl, it is necessary to establish a metadata structure using the metalite R package. For detailed instructions, please consult the metalite tutorial.

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.