diagnosePlp: diagnostic - Investigates the prediction problem settings -...

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

diagnostic - Investigates the prediction problem settings - use before training a model

Description

This function runs a set of prediction diagnoses to help pick a suitable T, O, TAR and determine whether the prediction problem is worth executing.

Usage

diagnosePlp(
  plpData = NULL,
  outcomeId,
  analysisId,
  populationSettings,
  splitSettings = createDefaultSplitSetting(),
  sampleSettings = createSampleSettings(),
  saveDirectory = NULL,
  featureEngineeringSettings = createFeatureEngineeringSettings(),
  modelSettings = setLassoLogisticRegression(),
  logSettings = createLogSettings(verbosity = "DEBUG", timeStamp = T, logName =
    "diagnosePlp Log"),
  preprocessSettings = createPreprocessSettings()
)

Arguments

plpData	An object of type plpData - the patient level prediction data extracted from the CDM. Can also include an initial population as plpData$popualtion.
outcomeId	(integer) The ID of the outcome.
analysisId	(integer) Identifier for the analysis. It is used to create, e.g., the result folder. Default is a timestamp.
populationSettings	An object of type populationSettings created using createStudyPopulationSettings that specifies how the data class labels are defined and addition any exclusions to apply to the plpData cohort
splitSettings	An object of type splitSettings that specifies how to split the data into train/validation/test. The default settings can be created using createDefaultSplitSetting.
sampleSettings	An object of type sampleSettings that specifies any under/over sampling to be done. The default is none.
saveDirectory	The path to the directory where the results will be saved (if NULL uses working directory)
featureEngineeringSettings	An object of featureEngineeringSettings specifying any feature engineering to be learned (using the train data)
modelSettings	An object of class modelSettings created using one of the function: setLassoLogisticRegression() A lasso logistic regression model setGradientBoostingMachine() A gradient boosting machine setAdaBoost() An ada boost model setRandomForest() A random forest model setDecisionTree() A decision tree model setKNN() A KNN model
logSettings	An object of logSettings created using createLogSettings specifying how the logging is done
preprocessSettings	An object of preprocessSettings. This setting specifies the minimum fraction of target population who must have a covariate for it to be included in the model training and whether to normalise the covariates before training

Details

Users can define set of Ts, Os, databases and population settings. A list of data.frames containing details such as follow-up time distribution, time-to-event information, characteriszation details, time from last prior event, observation time distribution.

Value

An object containing the model or location where the model is save, the data selection settings, the preprocessing and training settings as well as various performance measures obtained by the model.

distribution	list for each O of a data.frame containing: i) Time to observation end distribution, ii) Time from observation start distribution, iii) Time to event distribution and iv) Time from last prior event to index distribution (only for patients in T who have O before index)
incident	list for each O of incidence of O in T during TAR
characterization	list for each O of Characterization of T, TnO, Tn~O

Examples

## Not run: 
#******** EXAMPLE 1 ********* 

## End(Not run) 

OHDSI/PatientLevelPrediction documentation built on April 27, 2024, 8:11 p.m.