# Queries Dataset Documentation

# Introduction

To support the safety analysis, it is quite common to define specific grouping of events. One of the most common ways is to group events or medications by a specific medical concept such as a Standard MedDRA Queries (SMQs) or WHO-Drug Standardized Drug Groupings (SDGs).

To help with the derivation of these variables, the {admiral} function `derive_vars_query()` can be used. This function takes as input the dataset (`dataset`) where the grouping must occur (e.g `ADAE`) and a dataset containing the required information to perform the derivation of the grouping variables (`dataset_queries`).

The dataset passed to the `dataset_queries` argument of the `derive_vars_query()` function can be created by the `create_query_data()` function. For SMQs and SDGs company-specific functions for accessing the SMQ and SDG database need to be passed to the `create_query_data()` function (see the description of the `get_terms_fun` argument for details).

This vignette describes the expected structure and content of the dataset passed to the `dataset_queries` argument in the `derive_vars_query()` function.

# Structure of the Query Dataset

## Variables

Variable | Scope | Type | Example Value
--- | --- | --- | ---
PREFIX | The prefix used to define the grouping variables | Character | `"SMQ01"`
GRPNAME | The value provided to the grouping variables name | Character | `"Immune-Mediated Guillain-Barre Syndrome"`
SRCVAR | The variable used to define the grouping. Used in conjunction with TERMNAME | Character | `"AEDECOD"`
TERMNAME | A term used to define the grouping. Used in conjunction with SRCVAR | Character | `"GUILLAIN-BARRE SYNDROME"`
TERMID | A code used to define the grouping. Used in conjunction with SRCVAR | Integer | `10018767`
GRPID | Id number of the query. This could be a SMQ identifier | Integer | `20000131`
SCOPE | Scope (Broad/Narrow) of the query | Character | `BROAD`, `NARROW`, `NA`
SCOPEN | Scope (Broad/Narrow) of the query | Integer | `1`, `2`, `NA`
VERSION | The version of the dictionary | Character | `"20.1"`

Bold variables are required in `dataset_queries`: an error is issued if any of these variables is missing. Other variables are optional.

The `VERSION` variable is not used by `derive_vars_query()` but can be used to check if the dictionary version of the queries dataset and the analysis dataset are in line.

## Required Content

Each row must be unique within the dataset.

As described above, the variables `PREFIX`, `GRPNAME`, `SRCVAR`, `TERMNAME` and `TERMID` are required. The combination of these variables will allow the creation of the grouping variable.

### Input

- `PREFIX` must be a character string starting with 2 or 3 letters, followed by a 2-digits number (e.g. “CQ01”).

- `GRPNAME` must be a non missing character string and it must be unique within `PREFIX`.

- `SRCVAR` must be a non missing character string.

  - Each value in `SRCVAR` represents a variable from `dataset` used to define the grouping variables (e.g. `AEDECOD`,`AEBODSYS`, `AELLTCD`).

  - The function `derive_vars_query()` will check that each value given in `SRCVAR` has a corresponding variable in the input `dataset` and issue an error otherwise.

  - Different `SRCVAR` variables may be specified within a `PREFIX`.

- `TERMNAME` must be a character string. This must be populated if `TERMID` is missing.

- `TERMID` must be an integer. This must be populated if `TERMNAME` is missing.

### Output

- `PREFIX` will be used to create the grouping variable appending the suffix “NAM”. This variable will now be referred to as `ABCzzNAM`: the name of the grouping variable.

  - E.g. `PREFIX == "SMQ01"` will create the `SMQ01NAM` variable.

  - For each `PREFIX`, a new `ABCzzNAM` variable is created in `dataset`.

- `GRPNAME` will be used to populate the corresponding `ABCzzNAM` variable.

- `SRCVAR` will be used to identify the variables from `dataset` used to perform the grouping (e.g. `AEDECOD`,`AEBODSYS`, `AELLTCD`).

- `TERMNAME` (for character variables), `TERMID` (for numeric variables) will be used to identify the records meeting the criteria in `dataset` based on the variable defined in `SRCVAR`.

- Result:

  - For each record in `dataset`, where the variable defined by `SRCVAR` match a term from the `TERMNAME` (for character variables) or `TERMID` (for numeric variables) in the `datasets_queries`, `ABCzzNAM` is populated with `GRPNAME`.

  - Note: The type (numeric or character) of the variable defined in `SRCVAR` is checked in `dataset`. If the variable is a character variable (e.g. `AEDECOD`), it is expected that `TERMNAME` is populated, if it is a numeric variable (e.g. `AEBDSYCD`), it is expected that `TERMID` is populated, otherwise an error is issued.

### Example

In this example, one standard MedDRA query (`PREFIX = "SMQ01"`) and one customized query (`PREFIX = "CQ02"`) are defined to analyze the adverse events.

- The standard MedDRA query variable `SMQ01NAM` [`PREFIX`] will be populated with “Standard Query 1” [`GRPNAME`] if any preferred term (`AEDECOD`) [`SRCVAR`] in `dataset` is equal to “AE1” or “AE2” [`TERMNAME`]

- The customized query (`CQ02NAM`) [`PREFIX`] will be populated with “Query 2” [`GRPNAME`] if any Low Level Term Code (`AELLTCD`) [`SRCVAR`] in `dataset` is equal to 10 [`TERMID`] or any preferred term (`AEDECOD`) [`SRCVAR`] in `dataset` is equal to “AE4” [`TERMNAME`].

#### Query Dataset (`ds_query`)

PREFIX | GRPNAME | SRCVAR | TERMNAME | TERMID |
--- | --- | --- | --- | --- | ---
SMQ01 | Standard Query 1 | AEDECOD | AE1 |  |
SMQ01 | Standard Query 1 | AEDECOD | AE2 |  |
CQ02 | Query 2 | AELLTCD |  | 10 |
CQ02 | Query 2 | AEDECOD | AE4 |  |

#### Adverse Event Dataset (`ae`)

USUBJID | AEDECOD | AELLTCD
--- | --- | ---
0001 | AE1 | 101
0001 | AE3 | 10
0001 | AE4 | 120
0001 | AE5 | 130

#### Output Dataset

Generated by calling `derive_vars_query(dataset = ae, dataset_queries = ds_query)`.

USUBJID | AEDECOD | AELLTCD | SMQ01NAM | CQ02NAM
--- | --- | --- | --- | ---
0001 | AE1 | 101 | Standard Query 1 |
0001 | AE3 | 10 |  | Query 2
0001 | AE4 | 120 |  | Query 2
0001 | AE5 | 130 |  |

Subject 0001 has one event meeting the Standard Query 1 criteria (`AEDECOD = "AE1"`) and two events meeting the customized query (`AELLTCD = 10` and `AEDECOD = "AE4"`).

## Optional Content

When standardized MedDRA Queries are added to the dataset, it is expected that the name of the query (`ABCzzNAM`) is populated along with its number code (`ABCzzCD`), and its Broad or Narrow scope (`ABCzzSC`).

The following variables can be added to `queries_datset` to derive this information.

### Input

- `GRPID` must be an integer.

- `SCOPE` must be a character string. Possible values are: “BROAD”, “NARROW” or `NA`.

- `SCOPEN` must be an integer. Possible values are: `1`, `2` or `NA`.

### Output

- `GRPID`, `SCOPE` and `SCOPEN` will be used in the same way as `GRPNAME` (see here) and will help in the creation of the `ABCzzCD`, `ABCzzSC` and `ABCzzSCN` variables.

### Output Variables

These variables are optional and if not populated in `dataset_queries`, the corresponding output variable will not be created:

PREFIX | GRPNAME | GRPID | SCOPE | SCOPEN | Variables created
--- | --- | --- | --- | --- | ---
SMQ01 | Query 1 | XXXXXXXX | NARROW | 2 | `SMQ01NAM`, `SMQ01CD`, `SMQ01SC`, `SMQ01SCN`
SMQ02 | Query 2 | XXXXXXXX | BROAD |  | `SMQ02NAM`, `SMQ02CD`, `SMQ02SC`
SMQ03 | Query 3 | XXXXXXXX |  | 1 | `SMQ03NAM`, `SMQ03CD`, `SMQ03SCN`
SMQ04 | Query 4 | XXXXXXXX |  |  | `SMQ04NAM`, `SMQ04CD`
SMQ05 | Query 5 |  |  |  | `SMQ05NAM`