Adding Custom Sampling Functions

Introduction

This vignette describes how you can add your own custom function for sampling the target population in the Observational Health Data Sciencs and Informatics (OHDSI) PatientLevelPrediction package. This vignette assumes you have read and are comfortable with building single patient level prediction models as described in the BuildingPredictiveModels vignette.

We invite you to share your new sample functions with the OHDSI community through our GitHub repository.

Sample Function Code Structure

To make a sampling function that can be used within PatientLevelPrediction you need to write two different functions. The ‘create’ function and the ‘implement’ function.

The ‘create’ function, e.g., create<SampleFunctionName>, takes the parameters of the sample ‘implement’ function as input, checks these are valid and outputs these as a list of class ‘sampleSettings’ with the ‘fun’ attribute specifying the ‘implement’ function to call.

The ‘implement’ function, e.g., implement<SampleFunctionName>, must take as input: * trainData - a list containing: - covariateData: the plpData$covariateData restricted to the training patients - labels: a data frame that contain rowId (patient identifier) and outcomeCount (the class labels) - folds: a data.frame that contains rowId (patient identifier) and index (the cross validation fold) * sampleSettings - the output of your create<SampleFunctionName>

The ‘implement’ function can then do any manipulation of the trainData (such as undersampling or oversampling) but must output a trainData object containing the covariateData, labels and folds for the new training data sample.

Example

Let’s consider the situation where we wish to take a random sample of the training data population. To make this custom sampling function we need to write the ‘create’ and ‘implement’ R functions.

Create function

Our random sampling function will randomly sample n patients from the trainData. Therefore, the inputs for this are: * n an integer/double specifying the number of patients to sample * sampleSeed an integer/double specifying the seed for reproducibility

createRandomSampleSettings <- function(
                     n = 10000,
                     sampleSeed = sample(10000,1)
                     ){
  
  # add input checks
  checkIsClass(n, c('numeric','integer'))
  checkHigher(n,0)
  checkIsClass(sampleSeed, c('numeric','integer'))
  
  # create list of inputs to implement function
  sampleSettings <- list(
    n = n,
    sampleSeed  = sampleSeed 
    )
  
  # specify the function that will implement the sampling
  attr(sampleSettings, "fun") <- "implementRandomSampleSettings"

  # make sure the object returned is of class "sampleSettings"
  class(sampleSettings) <- "sampleSettings"
  return(sampleSettings)
  
}

We now need to create the ‘implement’ function implementRandomSampleSettings()

Implement function

All ‘implement’ functions must take as input the trainData and the sampleSettings (this is the output of the ‘create’ function). They must return a trainData object containing the covariateData, labels and folds.

In our example, the createRandomSampleSettings() will return a list with ‘n’ and ‘sampleSeed’. The sampleSettings therefore contains these.

implementRandomSampleSettings <- function(trainData, sampleSettings){

  n <- sampleSetting$n
  sampleSeed <- sampleSetting$sampleSeed
  
  if(n > nrow(trainData$labels)){
    stop('Sample n bigger than training population')
  }
  
  # set the seed for the randomization
  set.seed(sampleSeed)
  
  # now implement the code to do your desired sampling
  
  sampleRowIds <- sample(trainData$labels$rowId, n)
  
  sampleTrainData <- list()
  
  sampleTrainData$labels <- trainData$labels %>% 
    dplyr::filter(.data$rowId %in% sampleRowIds) %>% 
    dplyr::collect()
  
  sampleTrainData$folds <- trainData$folds %>% 
    dplyr::filter(.data$rowId %in% sampleRowIds) %>% 
    dplyr::collect()
  
  sampleTrainData$covariateData <- Andromeda::andromeda()
  sampleTrainData$covariateData$covariateRef <-trainData$covariateData$covariateRef
  sampleTrainData$covariateData$covariates <- trainData$covariateData$covariates %>% dplyr::filter(.data$rowId %in% sampleRowIds)
  
  #update metaData$populationSize 
  metaData <- attr(trainData$covariateData, 'metaData')
  metaData$populationSize = n
  attr(sampleTrainData$covariateData, 'metaData') <- metaData
  
  # make the cocvariateData the correct class
  class(sampleTrainData$covariateData) <- 'CovariateData'
  
  # return the updated trainData
  return(sampleTrainData)
}

Acknowledgments

Considerable work has been dedicated to provide the PatientLevelPrediction package.

citation("PatientLevelPrediction")

## To cite PatientLevelPrediction in publications use:
## 
##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
##   and implementation of a standardized framework to generate and
##   evaluate patient-level prediction models using observational
##   healthcare data." _Journal of the American Medical Informatics
##   Association_, *25*(8), 969-975.
##   <https://doi.org/10.1093/jamia/ocy032>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     author = {J. M. Reps and M. J. Schuemie and M. A. Suchard and P. B. Ryan and P. Rijnbeek},
##     title = {Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data},
##     journal = {Journal of the American Medical Informatics Association},
##     volume = {25},
##     number = {8},
##     pages = {969-975},
##     year = {2018},
##     url = {https://doi.org/10.1093/jamia/ocy032},
##   }

Please reference this paper if you use the PLP Package in your work:

Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25(8):969-975.

This work is supported in part through the National Science Foundation grant IIS 1251151.

Jenna Reps