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
#> ADaM metadata:
#> .$data_population Population data with 254 subjects
#> .$data_observation Observation data with 1191 records
#> .$plan Analysis plan with 10 plans
#>
#>
#> Analysis population type:
#> name id group var subset label
#> 1 'apat' 'USUBJID' 'TRTA' SAFFL == 'Y' 'All Participants as Treated'
#>
#>
#> Analysis observation type:
#> name id group var subset label
#> 1 'wk12' 'USUBJID' 'TRTA' SAFFL == 'Y' 'Weeks 0 to 12'
#> 2 'wk24' 'USUBJID' 'TRTA' AOCC01FL == 'Y' 'Weeks 0 to 24'
#>
#>
#> Analysis parameter type:
#> name label
#> 1 'rel' 'drug-related adverse events'
#> 2 'aeosi' 'adverse events of special interest'
#> 3 'any' 'any adverse events'
#> 4 'ser' 'serious adverse events'
#> subset
#> 1 AEREL %in% c('POSSIBLE', 'PROBABLE')
#> 2 AEOSI == 'Y'
#> 3
#> 4 AESER == 'Y'
#>
#>
#> Analysis function:
#> name label
#> 1 'ae_summary' 'Table: adverse event summary'
#> 2 'ae_specific' 'Table: specific adverse event'The object in meta is organized as a list of ADaM
mapping objects. For example, you can directly access an ADaM mapping
object as a list.
meta$population$apat
#> ADaM mapping:
#> * `name` -> "apat"
#> * `id` -> "USUBJID"
#> * `group` -> "TRTA"
#> * `var` -> NULL
#> * `subset` -> SAFFL == "Y"
#> * `label` -> "All Participants as Treated"We created helper function to help you access components by component
name. For example, we can collect the same apat component
from meta by name using
collect_adam_mapping().
Collect ADaM mapping for all participants as treated.
collect_adam_mapping(meta, name = "apat")
#> ADaM mapping:
#> * `name` -> "apat"
#> * `id` -> "USUBJID"
#> * `group` -> "TRTA"
#> * `var` -> NULL
#> * `subset` -> SAFFL == "Y"
#> * `label` -> "All Participants as Treated"
#> * `.location` -> "population"We can collect ADaM mapping for serious adverse events parameter
meta$parameter$ser.
collect_adam_mapping(meta, name = "ser")
#> ADaM mapping:
#> * `name` -> "ser"
#> * `id` -> NULL
#> * `group` -> NULL
#> * `var` -> "AEDECOD"
#> * `subset` -> AESER == "Y"
#> * `label` -> "serious adverse events"
#> * `soc` -> "AEBODSYS"
#> * `seq` -> 401
#> * `term1` -> "Serious"
#> * `term2` -> ""
#> * `summ_row` -> "with serious adverse events"
#> * `.location` -> "parameter"We can collect ADaM mapping for AE summary analysis method
meta$analysis$ae_summary.
collect_adam_mapping(meta, name = "ae_summary")
#> ADaM mapping:
#> * `name` -> "ae_summary"
#> * `id` -> NULL
#> * `group` -> NULL
#> * `var` -> NULL
#> * `subset` -> NULL
#> * `label` -> "Table: adverse event summary"
#> * `title` -> "Summary of Adverse Events"
#> * `.location` -> "analysis"While developing tools, developer also need to access the subset condition of a population. We can access the information from the list as
meta$population$apat$subset
#> SAFFL == "Y"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")
#> $population
#> [1] "SAFFL == 'Y'"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"
)
#> $population
#> [1] "SAFFL == 'Y'"
#>
#> $observation
#> [1] "SAFFL == 'Y'"
#>
#> $parameter
#> [1] "AESER == 'Y'"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"))
#> [1] 1 2 3 4 5 6Alternatively, 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"))
#> [1] "01-701-1015" "01-701-1023" "01-701-1028" "01-701-1033" "01-701-1034"
#> [6] "01-701-1047"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"))
#> USUBJID TRTA SAFFL
#> 1 01-701-1015 Placebo Y
#> 2 01-701-1023 Placebo Y
#> 3 01-701-1028 Xanomeline High Dose Y
#> 4 01-701-1033 Xanomeline Low Dose Y
#> 5 01-701-1034 Xanomeline High Dose Y
#> 6 01-701-1047 Placebo YThis example show how to add variables in population data set to display.
head(collect_population_record(meta, "apat", var = "AGE"))
#> USUBJID TRTA SAFFL AGE
#> 1 01-701-1015 Placebo Y 63
#> 2 01-701-1023 Placebo Y 64
#> 3 01-701-1028 Xanomeline High Dose Y 71
#> 4 01-701-1033 Xanomeline Low Dose Y 74
#> 5 01-701-1034 Xanomeline High Dose Y 77
#> 6 01-701-1047 Placebo Y 85We can also add add multiple additional variable to display
head(collect_population_record(meta, "apat", var = c("AGE", "TRT01P")))
#> USUBJID TRTA SAFFL AGE TRT01P
#> 1 01-701-1015 Placebo Y 63 Placebo
#> 2 01-701-1023 Placebo Y 64 Placebo
#> 3 01-701-1028 Xanomeline High Dose Y 71 Xanomeline High Dose
#> 4 01-701-1033 Xanomeline Low Dose Y 74 Xanomeline Low Dose
#> 5 01-701-1034 Xanomeline High Dose Y 77 Xanomeline High Dose
#> 6 01-701-1047 Placebo Y 85 PlaceboSimilarly 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")
#> [1] 689 1131 1173We 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")
#> USUBJID TRTA
#> 689 01-709-1424 Xanomeline High Dose
#> 1131 01-718-1170 Xanomeline Low Dose
#> 1173 01-718-1371 Xanomeline High Dose
#> AEDECOD SAFFL AESER
#> 689 SYNCOPE Y Y
#> 1131 SYNCOPE Y Y
#> 1173 PARTIAL SEIZURES WITH SECONDARY GENERALISATION Y YThis example show how to add variables in observation data set to display.
collect_observation_record(meta, "apat", "wk12", "ser", var = "AEDECOD")
#> USUBJID TRTA
#> 689 01-709-1424 Xanomeline High Dose
#> 1131 01-718-1170 Xanomeline Low Dose
#> 1173 01-718-1371 Xanomeline High Dose
#> AEDECOD SAFFL AESER
#> 689 SYNCOPE Y Y
#> 1131 SYNCOPE Y Y
#> 1173 PARTIAL SEIZURES WITH SECONDARY GENERALISATION Y YWe 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")
#> [1] "Summary of Adverse Events" "Weeks 0 to 12"
#> [3] "All Participants as Treated"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)
#> population observation
#> "adsl" "adae"