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Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query()

Source: R/create_query_data.R
create_query_data.Rd

Creates a queries dataset as input dataset to the dataset_queries argument in the derive_vars_query() function as defined in the Queries Dataset Documentation.

Usage

create_query_data(queries, version = NULL, get_terms_fun = NULL)

Arguments

queries

List of queries

A list of query() objects is expected.

version

Dictionary version

The dictionary version used for coding the terms should be specified. If any of the queries is a basket (SMQ, SDG, ....) or a customized query including a basket, the parameter needs to be specified.

Permitted Values: A character string (the expected format is company-specific)

get_terms_fun

Function which returns the terms

For each query specified for the queries parameter referring to a basket (i.e., those where the definition field is set to a basket_select() object or a list which contains at least one basket_select() object) the specified function is called to retrieve the terms defining the query. This function is not provided by admiral as it is company specific, i.e., it has to be implemented at company level.

The function must return a dataset with all the terms defining the basket. The output dataset must contain the following variables.

  • TERM_LEVEL: the variable to be used for defining a term of the basket, e.g., AEDECOD

  • TERM_NAME: the name of the term if the variable TERM_LEVEL is referring to is character

  • TERM_ID the numeric id of the term if the variable TERM_LEVEL is referring to is numeric

  • QUERY_NAME: the name of the basket. The values must be the same for all observations.

The function must provide the following parameters

  • basket_select: A basket_select() object.

  • version: The dictionary version. The value specified for the version in the create_query_data() call is passed to this parameter.

  • keep_id: If set to TRUE, the output dataset must contain the QUERY_ID variable. The variable must be set to the numeric id of the basket.

  • temp_env: A temporary environment is passed to this parameter. It can be used to store data which is used for all baskets in the create_query_data() call. For example if SMQs need to be read from a database all SMQs can be read and stored in the environment when the first SMQ is handled. For the other SMQs the terms can be retrieved from the environment instead of accessing the database again.

Value

A dataset to be used as input dataset to the dataset_queries

argument in derive_vars_query()

Details

For each query() object listed in the queries argument, the terms belonging to the query (TERM_LEVEL, TERM_NAME, TERM_ID) are determined with respect to the definition field of the query: if the definition field of the query() object is

  • a basket_select() object, the terms are read from the basket database by calling the function specified for the get_terms_fun parameter.

  • a data frame, the terms stored in the data frame are used.

  • a list of data frames and basket_select() objects, all terms from the data frames and all terms read from the basket database referenced by the basket_select() objects are collated.

The following variables (as described in Queries Dataset Documentation) are created:

  • VAR_PREFIX: Prefix of the variables to be created by derive_vars_query() as specified by the prefix element.

  • QUERY_NAME: Name of the query as specified by the name element.

  • QUERY_ID: Id of the query as specified by the id element. If the id element is not specified for a query, the variable is set to NA. If the id element is not specified for any query, the variable is not created.

  • QUERY_SCOPE: scope of the query as specified by the scope element of the basket_select() object. For queries not defined by a basket_select() object, the variable is set to NA. If none of the queries is defined by a basket_select() object, the variable is not created.

  • QUERY_SCOPE_NUM: numeric scope of the query. It is set to 1 if the scope is broad. Otherwise it is set to 2. If the add_scope_num element equals FALSE, the variable is set to NA. If the add_scope_num element equals FALSE for all baskets or none of the queries is an basket , the variable is not created.

  • TERM_LEVEL: Name of the variable used to identify the terms.

  • TERM_NAME: Value of the term variable if it is a character variable.

  • TERM_ID: Value of the term variable if it is a numeric variable.

  • VERSION: Set to the value of the version argument. If it is not specified, the variable is not created.

See also

derive_vars_query(), query(), basket_select(), Queries Dataset Documentation

Creating auxiliary datasets: consolidate_metadata(), create_period_dataset(), create_single_dose_dataset()

Examples

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(admiral.test)
library(admiral)

# creating a query dataset for a customized query
cqterms <- tribble(
  ~TERM_NAME, ~TERM_ID,
  "APPLICATION SITE ERYTHEMA", 10003041L,
  "APPLICATION SITE PRURITUS", 10003053L
) %>%
  mutate(TERM_LEVEL = "AEDECOD")

cq <- query(
  prefix = "CQ01",
  name = "Application Site Issues",
  definition = cqterms
)

create_query_data(queries = list(cq))
#> # A tibble: 2 x 5
#>   TERM_NAME                 TERM_ID TERM_LEVEL VAR_PREFIX QUERY_NAME            
#>   <chr>                       <int> <chr>      <chr>      <chr>                 
#> 1 APPLICATION SITE ERYTHE… 10003041 AEDECOD    CQ01       Application Site Issu…
#> 2 APPLICATION SITE PRURIT… 10003053 AEDECOD    CQ01       Application Site Issu…

# create a query dataset for SMQs
pregsmq <- query(
  prefix = "SMQ02",
  id = auto,
  definition = basket_select(
    name = "Pregnancy and neonatal topics (SMQ)",
    scope = "NARROW",
    type = "smq"
  )
)

bilismq <- query(
  prefix = "SMQ04",
  definition = basket_select(
    id = 20000121L,
    scope = "BROAD",
    type = "smq"
  )
)

# The get_terms function from admiral.test is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
  queries = list(pregsmq, bilismq),
  get_terms_fun = admiral.test:::get_terms,
  version = "20.1"
)
#> # A tibble: 43 x 7
#>    TERM_NAME     TERM_LEVEL QUERY_NAME   QUERY_ID QUERY_SCOPE VAR_PREFIX VERSION
#>    <chr>         <chr>      <chr>           <int> <chr>       <chr>      <chr>  
#>  1 Achromotrich… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  2 Craniosynost… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  3 Hypophosphat… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  4 Congenital p… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  5 Uterine cont… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  6 Ductus arter… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  7 Pseudotruncu… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  8 Lipomeningoc… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#>  9 Macrocephaly  AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#> 10 Carnitine pa… AEDECOD    Pregnancy a… 20000185 NARROW      SMQ02      20.1   
#> # … with 33 more rows

# create a query dataset for SDGs
sdg <- query(
  prefix = "SDG01",
  id = auto,
  definition = basket_select(
    name = "5-aminosalicylates for ulcerative colitis",
    scope = NA_character_,
    type = "sdg"
  )
)

# The get_terms function from admiral.test is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
  queries = list(sdg),
  get_terms_fun = admiral.test:::get_terms,
  version = "2019-09"
)
#> # A tibble: 16 x 7
#>    TERM_NAME     TERM_LEVEL QUERY_NAME   QUERY_ID QUERY_SCOPE VAR_PREFIX VERSION
#>    <chr>         <chr>      <chr>           <int> <chr>       <chr>      <chr>  
#>  1 AMINOSALICYL… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  2 AMINOSALICYL… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  3 AMINOSALICYL… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  4 AMINOSALICYL… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  5 SODIUM AMINO… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  6 AMINOSALICYL… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  7 SULFASALAZINE CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  8 CALCIUM BENZ… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#>  9 OLSALAZINE    CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#> 10 OLSALAZINE S… CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#> 11 MESALAZINE    CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#> 12 BALSALAZIDE   CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#> 13 BALSALAZIDE … CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#> 14 BALSALAZIDE … CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#> 15 DERSALAZINE   CMDECOD    5-aminosali…      220 NA          SDG01      2019-09
#> 16 DERSALAZINE … CMDECOD    5-aminosali…      220 NA          SDG01      2019-09

# creating a query dataset for a customized query including SMQs
# The get_terms function from admiral.test is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
  queries = list(
    query(
      prefix = "CQ03",
      name = "Special issues of interest",
      definition = list(
        basket_select(
          name = "Pregnancy and neonatal topics (SMQ)",
          scope = "NARROW",
          type = "smq"
        ),
        cqterms
      )
    )
  ),
  get_terms_fun = admiral.test:::get_terms,
  version = "20.1"
)
#> # A tibble: 23 x 6
#>    TERM_NAME              TERM_LEVEL QUERY_NAME       TERM_ID VAR_PREFIX VERSION
#>    <chr>                  <chr>      <chr>              <int> <chr>      <chr>  
#>  1 Achromotrichia congen… AEDECOD    Special issues …      NA CQ03       20.1   
#>  2 Craniosynostosis       AEDECOD    Special issues …      NA CQ03       20.1   
#>  3 Hypophosphatasia       AEDECOD    Special issues …      NA CQ03       20.1   
#>  4 Congenital pyelocalie… AEDECOD    Special issues …      NA CQ03       20.1   
#>  5 Uterine contractions … AEDECOD    Special issues …      NA CQ03       20.1   
#>  6 Ductus arteriosus pre… AEDECOD    Special issues …      NA CQ03       20.1   
#>  7 Pseudotruncus arterio… AEDECOD    Special issues …      NA CQ03       20.1   
#>  8 Lipomeningocele        AEDECOD    Special issues …      NA CQ03       20.1   
#>  9 Macrocephaly           AEDECOD    Special issues …      NA CQ03       20.1   
#> 10 Carnitine palmitoyltr… AEDECOD    Special issues …      NA CQ03       20.1   
#> # … with 13 more rows