Collect Components

In metalite: ADaM Metadata Structure

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)

library(metalite)

In this document, let's explore how to collect components using metalite. The document is for developer who plan to create new tools.

Let's first define a meta object by using the meta_example() function. It is an ad-hoc function to create a meta object same as in the get started page.

meta <- meta_example()
meta

Collect ADaM mapping

The object in meta is organized as a list of ADaM mapping objects. For example, you can directly access an ADaM mapping object as a list.

meta$population$apat

We created helper function to help you access components by component name. For example, we can collect the same apat component from meta by name using collect_adam_mapping().

Collect ADaM mapping for all participants as treated.

collect_adam_mapping(meta, name = "apat")

We can collect ADaM mapping for serious adverse events parameter meta$parameter$ser.

collect_adam_mapping(meta, name = "ser")

We can collect ADaM mapping for AE summary analysis method meta$analysis$ae_summary.

collect_adam_mapping(meta, name = "ae_summary")

Collect population

While developing tools, developer also need to access the subset condition of a population. We can access the information from the list as

meta$population$apat$subset

Equivalently, we can also use collect_population() to collect the definition of a population. For example, we can collect subset condition for apat as

collect_population(meta, population = "apat")

By using collect_population(), we can find subset condition from multiple levels. For example, we can collect analysis population definition for apat population, wk12 observation and ser parameters.

collect_population(meta,
  population = "apat",
  observation = "wk12",
  parameter = "ser"
)

Collect population records

User may want to identify records belong to an analysis population. We can use collect_population_index() to show all population record index. In this example, user would get index from all population data set for apat or all participants as treated.

population_index <- collect_population_index(meta, "apat")

head(collect_population_index(meta, "apat"))

Alternatively, people may want to know the ID of those subjects in a population. In this case, collect_population_id() can be used to collect ID - > USUBJID from the apat population.

population_id <- collect_population_id(meta, "apat")

head(collect_population_id(meta, "apat"))

We can further directly collect observations using collect_population_record(). By default, key variables used in id, group, and subset are displayed.

This example shows how to collect population record from population data set for all participants as treated and only display default variables.

head(collect_population_record(meta, "apat"))

This example show how to add variables in population data set to display.

head(collect_population_record(meta, "apat", var = "AGE"))

We can also add add multiple additional variable to display

head(collect_population_record(meta, "apat", var = c("AGE", "TRT01P")))

Collect observation record

Similarly we can collect observation records with examples below. This example shows how to collect observation record index from observation data set for serious AE from weeks 0 to 12 using collect_observation_index()

collect_observation_index(meta, "apat", "wk12", "ser")

We can also directly collect records using collect_observation_record(). By default, key variables used in id, group, and subset are displayed.

This example shows how to collect observation record from observation data set for all participants as treated from 0 to 12 week with serious AE and only display default variables.

collect_observation_record(meta, "apat", "wk12", "ser")

This example show how to add variables in observation data set to display.

collect_observation_record(meta, "apat", "wk12", "ser", var = "AEDECOD")

Collect specifications

We also provided helper functions to collect commonly used items

Developer can collect table title for analysis function meta information using collect_title().

collect_title(meta, "apat", "wk12", "ser", "ae_summary")

Developer can collect specification for data set name using collect_dataname(). It will show the data set name for population and observation.

collect_dataname(meta)

metalite documentation built on Sept. 11, 2024, 7:18 p.m.