R/Execute.R
executeTimeSeriesAnalyses.Rd
Extracts the time series data and runs the time series analyses on all (or a subset of) the cohorts. This function assumes the cohorts have already been generated using the OHDSI CohortGenerator package
executeTimeSeriesAnalyses(
connectionDetails = NULL,
connection = NULL,
cdmDatabaseSchema,
tempEmulationSchema = NULL,
cohortDatabaseSchema,
cohortTable = "cohort",
outputFolder,
databaseId,
cohortDefinitionSet,
tsDataFieldName = "subjectCount",
cohortTimeSeriesArgs,
tsAnalysisList
)
An object of type connectionDetails
as created using the
createConnectionDetails
function in the
DatabaseConnector package. Can be left NULL if connection
is
provided.
An object of type connection
as created using the
connect
function in the
DatabaseConnector package. Can be left NULL if connectionDetails
is provided, in which case a new connection will be opened at the start
of the function, and closed when the function finishes.
Schema name where your patient-level data in OMOP CDM format resides. Note that for SQL Server, this should include both the database and schema name, for example 'cdm_data.dbo'.
Some database platforms like Oracle and Impala do not truly support temp tables. To emulate temp tables, provide a schema with write privileges where temp tables can be created.
Schema name where your cohort table resides. Note that for SQL Server, this should include both the database and schema name, for example 'scratch.dbo'.
The name of the cohort table.
The location where the cohort time series data results will be written.
The database identifier for the time series data
The cohort definition set for the cohorts used for the analysis
The time series data field name to use. This will be either the "subjectCount" or "eventCount" as computed based on the cohort.
The cohort time series arguments. @seealso[createCohortTimeSeriesArgs] for more information.
A list of time series analyses as specified by using the @seealso [createSegmentedArgs] and/or @seealso [createOcpArgs]
The time series data for each cohort is extracted and stored to the file system. Then each cohort's time series is used to build one or more models.