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")
The forestly package creates interactive forest plots for clinical trial analysis & reporting.
- Safety analysis
- Specific adverse events analysis
- Efficacy analysis (future work)
- Subgroup analysis
forestly_screenrecording2.mp4
We assume ADaM datasets are ready for analysis and leverage metalite data structure to define inputs and outputs.
The general workflow is:
- Define input metadata from ADaM datasets with
metalite. prepare_ae_forestly()prepares datasets for interactive forest plot.format_ae_forestly()formats output layout.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()
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
$\blacktriangleright$ .
- Paper: 2023 PHUSE US Connect
- Talk: 2021 R/Pharma Conference