Interactive Forest Plot

Interactive forest plot for clinical trial safety analysis using 'metalite', 'reactable', 'plotly', and Analysis Data Model (ADaM) datasets. Includes functionality for adverse event filtering, incidence-based group filtering, hover-over reveals, and search and sort operations. The workflow allows for metadata construction, data preparation, output formatting, and interactive plot generation.

forestly

Installation

The easiest way to get forestly is to install from CRAN:

install.packages("forestly")

Alternatively, to use a new feature or get a bug fix, you can install the development version of forestly from GitHub:

# install.packages("remotes")
remotes::install_github("Merck/forestly")

Overview

The forestly package creates interactive forest plots for clinical trial analysis & reporting.

• Safety analysis
  • Specific adverse events analysis
• Efficacy analysis (future work)
  • Subgroup analysis

We assume ADaM datasets are ready for analysis and leverage metalite data structure to define inputs and outputs.

Workflow

The general workflow is:

1. Define input metadata from ADaM datasets with metalite.
2. prepare_ae_forestly() prepares datasets for interactive forest plot.
3. format_ae_forestly() formats output layout.
4. ae_forestly() generates an interactive forest plot.

Here is a quick example

library("forestly")

adsl <- forestly_adsl
adae <- forestly_adae
adsl$TRTA <- factor(
  adsl$TRT01A,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)
adae$TRTA <- factor(
  adae$TRTA,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)

analysis_plan <- metalite::plan(
  analysis = "ae_forestly",
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)

meta <- metalite::meta_adam(population = adsl, observation = adae) |>
  metalite::define_plan(plan = analysis_plan) |>
  metalite::define_population(
    name = "apat",
    var = c("USUBJID", "SAFFL", "TRTA", "SITEID", "SEX", "RACE", "AGE"),
    group = "TRTA",
    subset = SAFFL == "Y",
    label = "All Participants as Treated"
  ) |>
  metalite::define_observation(
    name = "wk12",
    var = c(
      "USUBJID", "SAFFL", "TRTA", "SITEID", "SEX", "RACE", "AGE",
      "ASTDY", "AEDECOD", "AEBODSYS", "AESER", "AEREL", "AEACN",
      "AEOUT", "ADURN", "ADURU"
    ),
    group = "TRTA",
    subset = SAFFL == "Y",
    label = "Weeks 0 to 12"
  ) |>
  metalite::define_parameter(
    name = "any",
    term1 = "",
    term2 = "",
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "All AEs"
  ) |>
  metalite::define_parameter(
    name = "rel",
    term1 = "Drug-Related",
    term2 = "",
    subset = AEREL %in% c("POSSIBLE", "PROBABLE"),
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "Drug-related AEs"
  ) |>
  metalite::define_parameter(
    name = "ser",
    term1 = "Serious",
    term2 = "",
    subset = AESER == "Y",
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "Serious AEs"
  ) |>
  metalite::define_analysis(
    name = "ae_forestly",
    label = "Interactive forest plot"
  ) |>
  metalite::meta_build()

meta |>
  prepare_ae_forestly(parameter = "any;rel;ser") |>
  format_ae_forestly() |>
  ae_forestly()

Interactive features

The interactive features for safety analysis include:

• Select different AE criteria.
• Filter by incidence of AE in one or more groups.
• Reveal information by hovering the mouse over a data point.
• Search bars to find subjects with selected adverse events (AEs).
• Sort value by clicking the column header.
• Drill-down listing by clicking $\blacktriangleright$.

References

• Paper: 2023 PHUSE US Connect
• Talk: 2021 R/Pharma Conference