This function executes a large set of SQL statements against the database in OMOP CDM format to extract the data needed to perform the analysis.

getDbCohortMethodData(
connectionDetails,
cdmDatabaseSchema,
oracleTempSchema = cdmDatabaseSchema,
targetId,
comparatorId,
outcomeIds,
studyStartDate = "",
studyEndDate = "",
exposureDatabaseSchema = cdmDatabaseSchema,
exposureTable = "drug_era",
outcomeDatabaseSchema = cdmDatabaseSchema,
outcomeTable = "condition_occurrence",
cdmVersion = "5",
excludeDrugsFromCovariates = TRUE,
firstExposureOnly = FALSE,
removeDuplicateSubjects = FALSE,
restrictToCommonPeriod = FALSE,
washoutPeriod = 0,
maxCohortSize = 0,
covariateSettings
)

## Arguments

connectionDetails An R object of typeconnectionDetails created using the function createConnectionDetails in the DatabaseConnector package. The name of the database schema that contains the OMOP CDM instance. Requires read permissions to this database. On SQL Server, this should specifiy both the database and the schema, so for example 'cdm_instance.dbo'. For Oracle only: the name of the database schema where you want all temporary tables to be managed. Requires create/insert permissions to this database. A unique identifier to define the target cohort. If exposureTable = DRUG_ERA, targetId is a CONCEPT_ID and all descendant concepts within that CONCEPT_ID will be used to define the cohort. If exposureTable <> DRUG_ERA, targetId is used to select the cohort_concept_id in the cohort-like table. A unique identifier to define the comparator cohort. If exposureTable = DRUG_ERA, comparatorId is a CONCEPT_ID and all descendant concepts within that CONCEPT_ID will be used to define the cohort. If exposureTable <> DRUG_ERA, comparatorId is used to select the cohort_concept_id in the cohort-like table. A list of cohort_definition_ids used to define outcomes. A calendar date specifying the minimum date that a cohort index date can appear. Date format is 'yyyymmdd'. A calendar date specifying the maximum date that a cohort index date can appear. Date format is 'yyyymmdd'. Important: the study end data is also used to truncate risk windows, meaning no outcomes beyond the study end date will be considered. The name of the database schema that is the location where the exposure data used to define the exposure cohorts is available. If exposureTable = DRUG_ERA, exposureDatabaseSchema is not used by assumed to be cdmSchema. Requires read permissions to this database. The tablename that contains the exposure cohorts. If exposureTable <> DRUG_ERA, then expectation is exposureTable has format of COHORT table: cohort_concept_id, SUBJECT_ID, COHORT_START_DATE, COHORT_END_DATE. The name of the database schema that is the location where the data used to define the outcome cohorts is available. If exposureTable = CONDITION_ERA, exposureDatabaseSchema is not used by assumed to be cdmSchema. Requires read permissions to this database. The tablename that contains the outcome cohorts. If outcomeTable <> CONDITION_OCCURRENCE, then expectation is outcomeTable has format of COHORT table: COHORT_DEFINITION_ID, SUBJECT_ID, COHORT_START_DATE, COHORT_END_DATE. Define the OMOP CDM version used: currently support "4" and "5". Should the target and comparator drugs (and their descendant concepts) be excluded from the covariates? Note that this will work if the drugs are actualy drug concept IDs (and not cohort IDs). Should only the first exposure per subject be included? Note that this is typically done in the createStudyPopulation function, but can already be done here for efficiency reasons. Remove subjects that are in both the target and comparator cohort? See details for allowed values.N ote that this is typically done in the createStudyPopulation function, but can already be done here for efficiency reasons. Restrict the analysis to the period when both treatments are observed? The mininum required continuous observation time prior to index date for a person to be included in the cohort. Note that this is typically done in the createStudyPopulation function, but can already be done here for efficiency reasons. If either the target or the comparator cohort is larger than this number it will be sampled to this size. maxCohortSize = 0 indicates no maximum size. An object of type covariateSettings as created using the createCovariateSettings function in the FeatureExtraction package.

## Value

Returns an object of type cohortMethodData, containing information on the cohorts, their outcomes, and baseline covariates. Information about multiple outcomes can be captured at once for efficiency reasons. This object is a list with the following components:

outcomes

A data frame listing the outcomes per person, including the time to event, and the outcome id. Outcomes are not yet filtered based on risk window, since this is done at a later stage.

cohorts

A data frame listing the persons in each cohort, listing their exposure status as well as the time to the end of the observation period and time to the end of the cohort (usually the end of the exposure era).

covariates

An ffdf object listing the baseline covariates per person in the two cohorts. This is done using a sparse representation: covariates with a value of 0 are omitted to save space.

covariateRef

An ffdf object describing the covariates that have been extracted.

A list of objects with information on how the cohortMethodData object was constructed.

The generic print() and summary() functions have been implemented for this object.

## Details

Based on the arguments, the treatment and comparator cohorts are retrieved, as well as outcomes occurring in exposed subjects. The treatment and comparator cohorts can be identified using the drug_era table, or through user-defined cohorts in a cohort table either inside the CDM instance or in a separate schema. Similarly, outcomes are identified using the condition_era table or through user-defined cohorts in a cohort table either inside the CDM instance or in a separate schema. Covariates are automatically extracted from the appropriate tables within the CDM. Important: The target and comparator drug must not be included in the covariates, including any descendant concepts. If the targetId and comparatorId arguments represent real concept IDs, you can set the excludeDrugsFromCovariates argument to TRUE and automatically the drugs and their descendants will be excluded from the covariates. However, if the targetId and comparatorId arguments do not represent concept IDs, you will need to manually add the drugs and descendants to the excludedCovariateConceptIds of the covariateSettings argument.

The removeduplicateSubjects argument can have one of the following values:

"keep all"

Do not remove subjects that appear in both target and comparator cohort

"keep first"

When a subjects appear in both target and comparator cohort, only keep whichever cohort is first in time.

"remove all"

Remove subjects that appear in both target and comparator cohort completely from the analysis."