Collect Components

The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Collect Components

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'

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
#> 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"

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
#> 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'"

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"))
#> [1] 1 2 3 4 5 6

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"))
#> [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     Y

This 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  85

We 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              Placebo

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")
#> [1]  689 1131 1173

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")
#>          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     Y

This 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     Y

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")
#> [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"

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.