createDataForEncoder: Create propensity scores

In ABMI/Prometheus: Methods to Enable Transferring Information across OHDSI

Description

Creates propensity scores using a regularized logistic regression.

Usage

createDataForEncoder(
  cohortMethodData,
  population = NULL,
  mapping = NULL,
  excludeCovariateIds = c(),
  includeCovariateIds = c(),
  maxCohortSizeForFitting = 250000,
  fileName = NULL,
  weight = "default",
  tidyCovariate = T
)

Arguments

cohortMethodData	An object of type [CohortMethodData] as generated using [getDbCohortMethodData()].
population	A data frame describing the population. This should at least have a 'rowId' column corresponding to the 'rowId' column in the [CohortMethodData] covariates object and a 'treatment' column. If population is not specified, the full population in the [CohortMethodData] will be used.
excludeCovariateIds	Exclude these covariates from the propensity model.
includeCovariateIds	Include only these covariates in the propensity model.
maxCohortSizeForFitting	If the target or comparator cohort are larger than this number, they will be downsampled before fitting the propensity model. The model will be used to compute propensity scores for all subjects. The purpose of the sampling is to gain speed. Setting this number to 0 means no downsampling will be applied.
removeRedundancy	If true, the function will remove the redundant covariates by using 'FeatureExtraction::tidyCovariateData' function. Default setting is true.

Examples

data(cohortMethodDataSimulationProfile)
cohortMethodData <- simulateCohortMethodData(cohortMethodDataSimulationProfile, n = 1000)
ps <- createPs(cohortMethodData)

ABMI/Prometheus documentation built on Sept. 29, 2020, 8:45 p.m.