covariateSummary: covariateSummary

In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

covariateSummary

Description

Summarises the covariateData to calculate the mean and standard deviation per covariate if the labels are given it also stratifies this by class label and if the trainRowIds and testRowIds specifying the patients in the train/test sets respectively are input, these values are also stratified by train and test set

Usage

covariateSummary(
  covariateData,
  cohort,
  labels = NULL,
  strata = NULL,
  variableImportance = NULL,
  featureEngineering = NULL
)

Arguments

covariateData	The covariateData part of the plpData that is extracted using getPlpData
cohort	The patient cohort to calculate the summary
labels	A data.frame with the columns rowId and outcomeCount
strata	A data.frame containing the columns rowId, strataName
variableImportance	A data.frame with the columns covariateId and value (the variable importance value)
featureEngineering	(currently not used ) A function or list of functions specifying any feature engineering to create covariates before summarising

Details

The function calculates various metrics to measure the performance of the model

Value

A data.frame containing: CovariateCount, CovariateMean and CovariateStDev for any specified stratification

Examples

data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n=100)
covariateSummary <- covariateSummary(plpData$covariateData, plpData$cohorts)
head(covariateSummary)

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.