tests/testthat/test-covariateSummary.R

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

# Copyright 2025 Observational Health Data Sciences and Informatics
#
# This file is part of PatientLevelPrediction
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

testCovariateData <- Andromeda::andromeda()
testCovariateData$covariates <- data.frame(
  rowId = c(1, 2, 5, 7, 10),
  covariateId = rep(1001, 5),
  covariateValue = rep(1, 5)
)

testCovariateData$covariateRef <- data.frame(
  covariateId = 1001,
  covariateName = "Made up covariate"
)

labels <- data.frame(
  rowId = 1:20,
  outcomeCount = c(rep(1, 5), rep(0, 15))
)


test_that("covariateSummary works with no strata or labels", {
  res <- covariateSummary(
    covariateData = testCovariateData,
    cohort = data.frame(rowId = 1:10),
    labels = NULL,
    strata = NULL,
    variableImportance = NULL,
    featureEngineering = NULL
  )

  expect_equal(res$CovariateCount, 5)
  expect_equal(res$CovariateMean, 0.5)
  expect_equal(res$CovariateStDev, 0.5)


  res <- covariateSummary(
    covariateData = testCovariateData,
    cohort = data.frame(rowId = 1:20),
    labels = NULL,
    strata = NULL,
    variableImportance = NULL,
    featureEngineering = NULL
  )

  expect_equal(res$CovariateCount, 5)
  expect_equal(res$CovariateMean, 0.25)
  expect_equal(res$CovariateStDev, sqrt((5 * (0.25 - 1)^2 + 15 * (0.25 - 0)^2) / 20))
})

test_that("covariateSummary works with labels", {
  res <- covariateSummary(
    covariateData = testCovariateData,
    cohort = data.frame(rowId = 1:20),
    labels = labels,
    strata = NULL,
    variableImportance = NULL,
    featureEngineering = NULL
  )

  expect_equal(res$WithNoOutcome_CovariateCount, 2)
  expect_equal(res$WithOutcome_CovariateCount, 3)

  expect_equal(res$WithNoOutcome_CovariateMean, 2 / 15)
  expect_equal(res$WithOutcome_CovariateMean, 3 / 5)

  expect_equal(res$WithNoOutcome_CovariateStDev, sqrt((2 * (2 / 15 - 1)^2 + 13 * (2 / 15)^2) / 15))
  expect_equal(res$WithOutcome_CovariateStDev, sqrt((3 * (3 / 5 - 1)^2 + 2 * (3 / 5)^2) / 5))

  expect_equal(abs(res$StandardizedMeanDiff), abs((2 / 15 - 3 / 5) / sqrt(((2 * (2 / 15 - 1)^2 + 13 * (2 / 15)^2) / 15 + (3 * (3 / 5 - 1)^2 + 2 * (3 / 5)^2) / 5) / 2)))
})