tests/testthat/test-recalibration.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
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prediction <- data.frame(
  rowId = 1:100,
  value = c(runif(20) / 30, runif(80) / 300),
  outcomeCount = c(runif(20) > 0.5, runif(80) > 0.9) * 1,
  gender = sample(c(8507, 1111), 100, replace = TRUE),
  ageYear = sample(1:100, 100, replace = TRUE),
  survivalTime = rep(365, 100),
  evaluationType = rep("Test", 100)
)

metaData <- list(
  modelType = "binary",
  targetId = 1,
  outcomeId = outcomeId,
  timepoint = 365
)

attr(prediction, "metaData") <- metaData

test_that("recalibrationInTheLarge", {
  test <- recalibratePlp(prediction,
    analysisId = "Analysis_1",
    method = "recalibrationInTheLarge"
  )

  expect_true(sum(test$evaluationType == "recalibrationInTheLarge") == 100)
})


#' weakRecalibration'
test_that("weakRecalibration", {
  test <- recalibratePlp(prediction,
    analysisId = "Analysis_1",
    method = "weakRecalibration"
  )

  expect_true(sum(test$evaluationType == "weakRecalibration") == 100)
})


test_that("recalibratePlpRefit", {
  skip_if_not_installed("Eunomia")
  skip_if_offline()
  newPop <- plpResult$prediction %>%
    dplyr::select(-"value") %>%
    dplyr::filter(.data$evaluationType %in% c("Test", "Train"))
  attr(newPop, "metaData") <- list(
    targetId = 1,
    outcomeId = outcomeId,
    restrictPlpDataSettings = PatientLevelPrediction::createRestrictPlpDataSettings(),
    populationSettings = PatientLevelPrediction::createStudyPopulationSettings()
  )

  testRecal <- recalibratePlpRefit(
    plpModel = plpResult$model,
    newPopulation = newPop,
    newData = plpData
  )

  if (!is.null(testRecal)) {
    expect_true(
      sum(testRecal$evaluationType == "recalibrationRefit") > 0
    )
    expect_s3_class(testRecal, "data.frame")
  }

  testRecalWithModel <- recalibratePlpRefit(
    plpModel = plpResult$model,
    newPopulation = newPop,
    newData = plpData,
    returnModel = TRUE
  )
  expect_type(testRecalWithModel, "list")
  expect_s3_class(testRecalWithModel$model, "plpModel")
  expect_s3_class(testRecalWithModel$prediction, "data.frame")

  # add more test...
})



test_that("survival", {
  # survival
  metaData <- list(
    modelType = "survival",
    targetId = 1,
    outcomeId = outcomeId,
    timepoint = 365
  )

  attr(prediction, "metaData") <- metaData

  test <- recalibratePlp(prediction,
    analysisId = "Analysis_1",
    method = "weakRecalibration"
  )

  expect_true(sum(test$evaluationType == "weakRecalibration") == 100)
})