assessPropensityModels: Assess propensity models

In OHDSI/Legend: Large-Scale Evidence Generation and Evaluation in a Network of Databases

Description

Assess propensity models

Usage

assessPropensityModels(connectionDetails, cdmDatabaseSchema,
  cohortDatabaseSchema, tablePrefix = "legend",
  indicationId = "Depression", oracleTempSchema, outputFolder,
  sampleSize = 1000, maxCores = 4, databaseId)

Arguments

connectionDetails	An object of type connectionDetails as created using the createConnectionDetails function in the DatabaseConnector package.
cdmDatabaseSchema	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'.
cohortDatabaseSchema	Schema name where intermediate data can be stored. You will need to have write priviliges in this schema. Note that for SQL Server, this should include both the database and schema name, for example 'cdm_data.dbo'.
tablePrefix	A prefix to be used for all table names created for this study.
indicationId	A string denoting the indicationId for which the exposure cohorts should be created.
oracleTempSchema	Should be used in Oracle to specify a schema where the user has write priviliges for storing temporary tables.
outputFolder	Name of local folder to place results; make sure to use forward slashes (/)
sampleSize	What is the maximum sample size for each exposure cohort?
maxCores	How many parallel cores should be used? If more cores are made available this can speed up the analyses.
databaseId	A short string for identifying the database (e.g. 'Synpuf').

Details

This function will sample the exposure cohorts, and fit propensity models to identify issues. Assumes the exposure and outcome cohorts have already been created.

OHDSI/Legend documentation built on Dec. 29, 2020, 3:52 a.m.