createEncodedPs: Create propensity scores
In ABMI/Prometheus: Methods to Enable Transferring Information across OHDSI

Description Usage Arguments Examples

View source: R/MetisCohortMethod.R

Creates propensity scores using a regularized logistic regression.

createEncodedPs(
  cohortMethodData,
  encoder = NULL,
  population = NULL,
  excludeCovariateIds = c(),
  includeCovariateIds = c(),
  maxCohortSizeForFitting = 250000,
  errorOnHighCorrelation = TRUE,
  stopOnError = TRUE,
  prior = createPrior("laplace", exclude = c(0), useCrossValidation = TRUE),
  control = createControl(noiseLevel = "silent", cvType = "auto", seed = 1, tolerance =
    2e-07, cvRepetitions = 10, startingVariance = 0.01),
  removeRedundancy = T,
  cmEncoder = NULL
)

`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.
`errorOnHighCorrelation`	If true, the function will test each covariate for correlation with the treatment assignment. If any covariate has an unusually high correlation (either positive or negative), this will throw and error.
`stopOnError`	If an error occurr, should the function stop? Else, the two cohorts will be assumed to be perfectly separable.
`prior`	The prior used to fit the model. See [Cyclops::createPrior()] for details.
`control`	The control object used to control the cross-validation used to determine the hyperparameters of the prior (if applicable). See [Cyclops::createControl()] for details.
`removeRedundancy`	If true, the function will remove the redundant covariates by using 'FeatureExtraction::tidyCovariateData' function. Default setting is true.

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