Interactive Forest Plot

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.

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

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

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

AE Criteria

Incidence (%) in One or More Treatment Groups

Download as CSV

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",
  parameter_term = "any;rel;ser"
)
#> ADaM metadata: 
#>    .$data_population     Population data with 170 subjects 
#>    .$data_observation    Observation data with 736 records 
#>    .$plan    Analysis plan with 1 plans 
#> 
#> 
#>   Analysis population type:
#>     name        id  group var       subset label
#> 1 'apat' 'USUBJID' 'TRTA'     SAFFL == 'Y'    ''
#> 
#> 
#>   Analysis observation type:
#>     name        id  group var       subset label
#> 1 'wk12' 'USUBJID' 'TRTA'     SAFFL == 'Y'    ''
#> 
#> 
#>   Analysis parameter type:
#>    name                         label                     subset
#> 1 'any'          'any adverse events'                           
#> 2 'rel' 'drug-related adverse events' toupper(AREL) == 'RELATED'
#> 3 'ser'      'serious adverse events'               AESER == 'Y'
#> 
#> 
#>   Analysis function:
#>            name                     label
#> 1 'ae_forestly' 'Interactive forest plot'

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
#> ADaM metadata: 
#>    .$data_population     Population data with 170 subjects 
#>    .$data_observation    Observation data with 736 records 
#>    .$plan    Analysis plan with 1 plans 
#> 
#> 
#>   Analysis population type:
#>    name        id  group var       subset label
#> 1 'ITT' 'USUBJID' 'TRTA'     ITTFL == 'Y'    ''
#> 
#> 
#>   Analysis observation type:
#>     name        id  group var         subset label
#> 1 'TEAE' 'USUBJID' 'TRTA'     TRTEMFL == 'Y'    ''
#> 
#> 
#>   Analysis parameter type:
#>       name                         label                      subset
#> 1    'any'          'any adverse events'                            
#> 2    'ser'      'serious adverse events'                AESER == 'Y'
#> 3 'nonser'  'non-serious adverse events' AESER != 'Y' | is.na(AESER)
#> 4    'rel' 'drug-related adverse events'  toupper(AREL) == 'RELATED'
#> 
#> 
#>   Analysis function:
#>            name                     label
#> 1 'ae_forestly' 'Interactive forest plot'

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()

AE Criteria

Incidence (%) in One or More Treatment Groups

Download as CSV

In this example we select any, rel and ser.

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

AE Criteria

Incidence (%) in One or More Treatment Groups

Download as CSV

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 = "TRTA",
    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
#> ADaM metadata: 
#>    .$data_population     Population data with 170 subjects 
#>    .$data_observation    Observation data with 736 records 
#>    .$plan    Analysis plan with 2 plans 
#> 
#> 
#>   Analysis population type:
#>     name        id  group var       subset                         label
#> 1 'apat' 'USUBJID' 'TRTA'     SAFFL == 'Y' 'All Participants as Treated'
#> 
#> 
#>   Analysis observation type:
#>        name        id  group       var                        subset
#> 1    'wk12' 'USUBJID' 'TRTA'                            SAFFL == 'Y'
#> 2 'period1' 'USUBJID' 'TRTA' 'AEDECOD' TRTEMFL == 'Y' & SAFFL == 'Y'
#>             label
#> 1 'Weeks 0 to 12'
#> 2      'Period 1'
#> 
#> 
#>   Analysis parameter type:
#>    name                         label                               subset
#> 1 'rel' 'drug-related adverse events' AEREL %in% c('POSSIBLE', 'PROBABLE')
#> 2 'any'          'any adverse events'                                     
#> 3 'ser'      'serious adverse events'                         AESER == 'Y'
#> 
#> 
#>   Analysis function:
#>            name                     label
#> 1 'ae_forestly' 'Interactive forest plot'

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()

AE Criteria

Incidence (%) in One or More Treatment Groups

Download as CSV

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()

AE Criteria

Incidence (%) in One or More Treatment Groups

Download as CSV