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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.

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

  • TERMCHAR: the name of the term if the variable SRCVAR is referring to is character

  • TERMNUM the numeric id of the term if the variable SRCVAR is referring to is numeric

  • GRPNAME: 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 GRPID 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 (SRCVAR, TERMCHAR, TERMNUM) 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:

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

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

  • GRPID: 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.

  • 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.

  • SCOPEN: 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.

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

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

  • TERMNUM: 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(pharmaversesdtm)
library(admiral)

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

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

create_query_data(queries = list(cq))
#> # A tibble: 2 × 5
#>   TERMCHAR                   TERMNUM SRCVAR  PREFIX GRPNAME                
#>   <chr>                        <int> <chr>   <chr>  <chr>                  
#> 1 APPLICATION SITE ERYTHEMA 10003041 AEDECOD CQ01   Application Site Issues
#> 2 APPLICATION SITE PRURITUS 10003053 AEDECOD CQ01   Application Site Issues

# 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 pharmaversesdtm 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 = pharmaversesdtm:::get_terms,
  version = "20.1"
)
#> # A tibble: 43 × 7
#>    TERMCHAR                           SRCVAR GRPNAME  GRPID SCOPE PREFIX VERSION
#>    <chr>                              <chr>  <chr>    <int> <chr> <chr>  <chr>  
#>  1 Achromotrichia congenital          AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  2 Craniosynostosis                   AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  3 Hypophosphatasia                   AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  4 Congenital pyelocaliectasis        AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  5 Uterine contractions during pregn… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  6 Ductus arteriosus premature closu… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  7 Pseudotruncus arteriosus           AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  8 Lipomeningocele                    AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#>  9 Macrocephaly                       AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#> 10 Carnitine palmitoyltransferase de… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1   
#> # ℹ 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 pharmaversesdtm 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 = pharmaversesdtm:::get_terms,
  version = "2019-09"
)
#> # A tibble: 16 × 7
#>    TERMCHAR                            SRCVAR GRPNAME GRPID SCOPE PREFIX VERSION
#>    <chr>                               <chr>  <chr>   <int> <chr> <chr>  <chr>  
#>  1 AMINOSALICYLIC ACID                 CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  2 AMINOSALICYLATE CALCIUM             CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  3 AMINOSALICYLATE CALCIUM ALUMINIUM   CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  4 AMINOSALICYLATE SODIUM              CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  5 SODIUM AMINOSALICYLATE DIHYDRATE    CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  6 AMINOSALICYLATE SODIUM;AMINOSALICY… CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  7 SULFASALAZINE                       CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  8 CALCIUM BENZAMIDOSALICYLATE         CMDEC… 5-amin…   220 NA    SDG01  2019-09
#>  9 OLSALAZINE                          CMDEC… 5-amin…   220 NA    SDG01  2019-09
#> 10 OLSALAZINE SODIUM                   CMDEC… 5-amin…   220 NA    SDG01  2019-09
#> 11 MESALAZINE                          CMDEC… 5-amin…   220 NA    SDG01  2019-09
#> 12 BALSALAZIDE                         CMDEC… 5-amin…   220 NA    SDG01  2019-09
#> 13 BALSALAZIDE SODIUM                  CMDEC… 5-amin…   220 NA    SDG01  2019-09
#> 14 BALSALAZIDE DISODIUM DIHYDRATE      CMDEC… 5-amin…   220 NA    SDG01  2019-09
#> 15 DERSALAZINE                         CMDEC… 5-amin…   220 NA    SDG01  2019-09
#> 16 DERSALAZINE SODIUM                  CMDEC… 5-amin…   220 NA    SDG01  2019-09

# creating a query dataset for a customized query including SMQs
# The get_terms function from pharmaversesdtm 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 = pharmaversesdtm:::get_terms,
  version = "20.1"
)
#> # A tibble: 23 × 6
#>    TERMCHAR                                SRCVAR GRPNAME TERMNUM PREFIX VERSION
#>    <chr>                                   <chr>  <chr>     <int> <chr>  <chr>  
#>  1 Achromotrichia congenital               AEDEC… Specia…      NA CQ03   20.1   
#>  2 Craniosynostosis                        AEDEC… Specia…      NA CQ03   20.1   
#>  3 Hypophosphatasia                        AEDEC… Specia…      NA CQ03   20.1   
#>  4 Congenital pyelocaliectasis             AEDEC… Specia…      NA CQ03   20.1   
#>  5 Uterine contractions during pregnancy   AEDEC… Specia…      NA CQ03   20.1   
#>  6 Ductus arteriosus premature closure     AEDEC… Specia…      NA CQ03   20.1   
#>  7 Pseudotruncus arteriosus                AEDEC… Specia…      NA CQ03   20.1   
#>  8 Lipomeningocele                         AEDEC… Specia…      NA CQ03   20.1   
#>  9 Macrocephaly                            AEDEC… Specia…      NA CQ03   20.1   
#> 10 Carnitine palmitoyltransferase deficie… AEDEC… Specia…      NA CQ03   20.1   
#> # ℹ 13 more rows