In elong0527/forestly: Interactive Forest Plot

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  eval = TRUE,
  echo = TRUE
)

library(metalite)
library(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.

meta_forestly(
  dataset_adsl = forestly_adsl,
  dataset_adae = forestly_adae,
  population_term = "apat",
  observation_term = "wk12"
) |>
  prepare_ae_forestly(parameter = "any;rel;ser") |>
  format_ae_forestly() |>
  ae_forestly()

Interactive features

The interactive features of the example include:

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

Workflow

The general workflow is:

1. meta_forestly() constructs input metadata for treatment analysis from ADaM datasets.
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 meta_forestly() is a wrapper function of the metalite package. The function is for users to simplify the process to define input parameters required to generate the interactive forest plot.

meta_forestly function require dataset_adsl, dataset_adae, observation_term and population_term arguments. dataset_adsl, dataset_adae are the input ADaM dataset for population and observation. In this example, we will use "apat" for population_term and "wk12" for observation_term. For subset creteria for both terms, we are using SAFFL == "Y" by default in meta_forestly().

meta_forestly(
  dataset_adsl = forestly_adsl,
  dataset_adae = forestly_adae,
  population_term = "apat",
  observation_term = "wk12"
)

If user wants to customize these definitions of terms or define new terms, more tutorials can be found at this link.

For paramter level metadata, parameter_term is selected from predefined terms listed below. By default, we choose three parameter terms rel, any and ser and use ";" to concatenate them to allow the three types of AE to be displayed in one interactive forest plot.

| Term | Subset Criteria | Label | | :------------- |:------------- | :-----| | any | NULL | any adverse events | | rel | toupper(AREL) == "RELATED" | drug-related adverse events | | g34 | ATOXGRN %in% c(3, 4)| grade 3-4 adverse events | | g340rel | ATOXGRN %in% c(3, 4) & AREL == "Y"| drug-related grade 3-4 adverse events | | g35 | ATOXGRN %in% c(3, 4, 5)| grade 3-5 adverse events | | g350rel | ATOXGRN %in% c(3, 4, 5) & AREL == "Y"| drug-related grade 3-5 adverse events | | nonser | AESER != "Y" | is.na(AESER)| non-serious adverse events | | ser | AESER == "Y"| serious adverse events | | ser0rel | AESER == "Y" & AREL == "Y"| serious drug-related adverse events | | mod | toupper(AEACN) %in% c("DOSE REDUCED", "DRUG INTERRUPTED", "DRUG WITHDRAWN")| adverse events result in dose modification | | dth | AESDTH == "Y"| adverse events result in death | | dtc0rel | AESDTH == "Y" & AREL == "Y"| drug-related adverse events result in death | | disc | toupper(AEACN) == "DRUG WITHDRAWN"| adverse events resulting in discontinuation | | disc0drel | toupper(AEACN) == "DRUG WITHDRAWN" & AREL == "Y"| drug-related adverse events resulting in discontinuation | | disc0ser | toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y"| serious adverse events resulting in discontinuation | | disc0ser0rel | toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y" & AREL == "Y"| serious drug-related adverse events resulting in discontinuations | | inj | toupper(AECAT) == "I"| injection-site | | noninj | toupper(AECAT) != "I" | is.na(AECAT) | non-injection-site| | inj0rel | toupper(AECAT) == "I" & AREL == "Y" | drug-related injection-site | | noninj0rel | (toupper(AECAT) == "I" | is.na(AECAT)) & AREL == "Y" | drug-related non-injection-site |

In the following example, we define ITT with subset criteria ITTFL == "Y"for population and TEAE with subset criteria TRTEMFL == "Y" for observation. For parameter, we choose ser and nonser from pre-defined parameter list.

meta <- meta_forestly(
  dataset_adsl = forestly_adsl,
  dataset_adae = forestly_adae,
  population_term = "ITT",
  population_subset = ITTFL == "Y",
  observation_term = "TEAE",
  observation_subset = TRTEMFL == "Y",
  parameter_term = "any;ser;nonser;rel"
)
meta

After the metadata is defined, we can create an interactive forest plot as below.

In this section, we calculate statistics and prepare plotting dataset using prepare_ae_forestly(). Argument parameter is required since we can select parameter from the one we defined in metadata.

Then we could use format_ae_forestly() to format the output and ae_forestly() to create the interactive forest plot. Default value is used for all arguments in these two functions.

In the first example, we select any, ser and nonser.

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

In this example we select any, rel and ser.

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

Define metadata

library(metalite)

In order to provide more flexibility for different analysis, The forestly package also allows other way to construct metadata. We only show a simplified example for the metalite package. More details can be found in the metalite package website.

Then we start to construct a metadata using metalite. The output metadata contains two observation wk12 and period1 as illustration purpose. Also, for parameter, the subset criteria is AEREL %in% c("POSSIBLE", "PROBABLE") instead of toupper(AREL) == "RELATED" in order to fit the observation dataset.

meta <- meta_adam(
  population = forestly_adsl,
  observation = forestly_adae
) |>
  define_plan(plan = plan(
    analysis = "ae_forestly",
    population = "apat",
    observation = c("wk12", "period1"),
    parameter = "any;rel;ser"
  )) |>
  define_population(
    name = "apat",
    group = "TRT01A",
    subset = quote(SAFFL == "Y")
  ) |>
  define_observation(
    name = "wk12",
    group = "TRTA",
    subset = quote(SAFFL == "Y"),
    label = "Weeks 0 to 12"
  ) |>
  define_observation(
    name = "period1",
    group = "TRTA",
    var = "AEDECOD",
    subset = quote(TRTEMFL == "Y" & SAFFL == "Y"),
    label = "Period 1"
  ) |>
  define_parameter(
    name = "rel",
    subset = quote(AEREL %in% c("POSSIBLE", "PROBABLE"))
  ) |>
  define_analysis(
    name = "ae_forestly",
    label = "Interactive forest plot"
  ) |>
  meta_build()

meta

After the metadata is defined, we can create an interactive forest plot for wk12 as below.

meta |>
  prepare_ae_forestly(
    population = "apat",
    observation = "wk12",
    parameter = "any;rel;ser"
  ) |>
  format_ae_forestly() |>
  ae_forestly()

Using similar approach, we could create an interactive forest plot for period1.

meta |>
  prepare_ae_forestly(
    population = "apat",
    observation = "period1",
    parameter = "any;rel;ser"
  ) |>
  format_ae_forestly() |>
  ae_forestly()

elong0527/forestly documentation built on July 4, 2023, 6:54 p.m.