Develop Clinical Prediction Models Using the Common Data Model

[Up] [Top]

Documentation for package ‘PatientLevelPrediction’ version 6.4.0

Help Pages

A B C D E F G I L M O P R S T V

-- A --

averagePrecision Calculate the average precision

-- B --

brierScore brierScore

-- C --

calibrationLine calibrationLine
computeAuc Compute the area under the ROC curve
computeGridPerformance Computes grid performance with a specified performance function
configurePython Sets up a python environment to use for PLP (can be conda or venv)
covariateSummary covariateSummary
createCohortCovariateSettings Extracts covariates based on cohorts
createDatabaseDetails Create a setting that holds the details about the cdmDatabase connection for data extraction
createDatabaseSchemaSettings Create the PatientLevelPrediction database result schema settings
createDefaultExecuteSettings Creates default list of settings specifying what parts of runPlp to execute
createDefaultSplitSetting Create the settings for defining how the plpData are split into test/validation/train sets using default splitting functions (either random stratified by outcome, time or subject splitting)
createExecuteSettings Creates list of settings specifying what parts of runPlp to execute
createExistingSplitSettings Create the settings for defining how the plpData are split into test/validation/train sets using an existing split - good to use for reproducing results from a different run
createFeatureEngineeringSettings Create the settings for defining any feature engineering that will be done
createGlmModel createGlmModel
createIterativeImputer Create Iterative Imputer settings
createLearningCurve createLearningCurve
createLogSettings Create the settings for logging the progression of the analysis
createModelDesign Specify settings for developing a single model
createNormalizer Create the settings for normalizing the data @param type The type of normalization to use, either "minmax" or "robust"
createPlpResultTables Create the results tables to store PatientLevelPrediction models and results into a database
createPreprocessSettings Create the settings for preprocessing the trainData.
createRandomForestFeatureSelection Create the settings for random foreat based feature selection
createRareFeatureRemover Create the settings for removing rare features
createRestrictPlpDataSettings createRestrictPlpDataSettings define extra restriction settings when calling getPlpData
createSampleSettings Create the settings for defining how the trainData from 'splitData' are sampled using default sample functions.
createSimpleImputer Create Simple Imputer settings
createSklearnModel Plug an existing scikit learn python model into the PLP framework
createSplineSettings Create the settings for adding a spline for continuous variables
createStratifiedImputationSettings Create the settings for using stratified imputation.
createStudyPopulation Create a study population
createStudyPopulationSettings create the study population settings
createTempModelLoc Create a temporary model location
createUnivariateFeatureSelection Create the settings for defining any feature selection that will be done
createValidationDesign createValidationDesign - Define the validation design for external validation
createValidationSettings createValidationSettings define optional settings for performing external validation

-- D --

diagnoseMultiplePlp Run a list of predictions diagnoses
diagnosePlp diagnostic - Investigates the prediction problem settings - use before training a model

-- E --

evaluatePlp evaluatePlp
externalValidateDbPlp externalValidateDbPlp - Validate a model on new databases
extractDatabaseToCsv Exports all the results from a database into csv files

-- F --

fitPlp fitPlp

-- G --

getCalibrationSummary Get a sparse summary of the calibration
getCohortCovariateData Extracts covariates based on cohorts
getDemographicSummary Get a demographic summary
getEunomiaPlpData Create a plpData object from the Eunomia database'
getPlpData Extract the patient level prediction data from the server
getPredictionDistribution Calculates the prediction distribution
getThresholdSummary Calculate all measures for sparse ROC

-- I --

ici Calculate the Integrated Calibration Index from Austin and Steyerberg https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281
insertCsvToDatabase Function to insert results into a database from csvs
insertResultsToSqlite Create sqlite database with the results

-- L --

listAppend join two lists
listCartesian Cartesian product
loadPlpAnalysesJson Load the multiple prediction json settings from a file
loadPlpData Load the plpData from a folder
loadPlpModel loads the plp model
loadPlpResult Loads the evalaution dataframe
loadPlpShareable Loads the plp result saved as json/csv files for transparent sharing
loadPrediction Loads the prediction dataframe to json

-- M --

MapIds Map covariate and row Ids so they start from 1
migrateDataModel Migrate Data model
modelBasedConcordance Calculate the model-based concordance, which is a calculation of the expected discrimination performance of a model under the assumption the model predicts the "TRUE" outcome as detailed in van Klaveren et al. https://pubmed.ncbi.nlm.nih.gov/27251001/

-- O --

outcomeSurvivalPlot Plot the outcome incidence over time

-- P --

pfi Permutation Feature Importance
plotDemographicSummary Plot the Observed vs. expected incidence, by age and gender
plotF1Measure Plot the F1 measure efficiency frontier using the sparse thresholdSummary data frame
plotGeneralizability Plot the train/test generalizability diagnostic
plotLearningCurve plotLearningCurve
plotNetBenefit Plot the net benefit
plotPlp Plot all the PatientLevelPrediction plots
plotPrecisionRecall Plot the precision-recall curve using the sparse thresholdSummary data frame
plotPredictedPDF Plot the Predicted probability density function, showing prediction overlap between true and false cases
plotPredictionDistribution Plot the side-by-side boxplots of prediction distribution, by class
plotPreferencePDF Plot the preference score probability density function, showing prediction overlap between true and false cases #'
plotSmoothCalibration Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016)
plotSparseCalibration Plot the calibration
plotSparseCalibration2 Plot the conventional calibration
plotSparseRoc Plot the ROC curve using the sparse thresholdSummary data frame
plotVariableScatterplot Plot the variable importance scatterplot
predictCyclops Create predictive probabilities
predictGlm predict using a logistic regression model
predictPlp predictPlp
preprocessData A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features
print.plpData Print a plpData object
print.summary.plpData Print a summary.plpData object

-- R --

recalibratePlp recalibratePlp
recalibratePlpRefit recalibratePlpRefit
runMultiplePlp Run a list of predictions analyses
runPlp runPlp - Develop and internally evaluate a model using specified settings

-- S --

savePlpAnalysesJson Save the modelDesignList to a json file
savePlpData Save the plpData to folder
savePlpModel Saves the plp model
savePlpResult Saves the result from runPlp into the location directory
savePlpShareable Save the plp result as json files and csv files for transparent sharing
savePrediction Saves the prediction dataframe to a json file
setAdaBoost Create setting for AdaBoost with python DecisionTreeClassifier base estimator
setCoxModel Create setting for lasso Cox model
setDecisionTree Create setting for the scikit-learn DecisionTree with python
setGradientBoostingMachine Create setting for gradient boosting machine model using gbm_xgboost implementation
setIterativeHardThresholding Create setting for Iterative Hard Thresholding model
setLassoLogisticRegression Create modelSettings for lasso logistic regression
setLightGBM Create setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package).
setMLP Create setting for neural network model with python's scikit-learn. For bigger models, consider using 'DeepPatientLevelPrediction' package.
setNaiveBayes Create setting for naive bayes model with python
setPythonEnvironment Use the python environment created using configurePython()
setRandomForest Create setting for random forest model using sklearn
setSVM Create setting for the python sklearn SVM (SVC function)
simulatePlpData Generate simulated data
simulationProfile A simulation profile for generating synthetic patient level prediction data
sklearnFromJson Loads sklearn python model from json
sklearnToJson Saves sklearn python model object to json in path
splitData Split the plpData into test/train sets using a splitting settings of class 'splitSettings'
summary.plpData Summarize a plpData object

-- T --

toSparseM Convert the plpData in COO format into a sparse R matrix

-- V --

validateExternal validateExternal - Validate model performance on new data
validateMultiplePlp externally validate the multiple plp models across new datasets
viewDatabaseResultPlp open a local shiny app for viewing the result of a PLP analyses from a database
viewMultiplePlp open a local shiny app for viewing the result of a multiple PLP analyses
viewPlp viewPlp - Interactively view the performance and model settings