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

Source: R/DiagnosePlp.R
diagnosePlp.Rd

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 = TRUE, 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

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

Value

An object containing the model or location where the model is saved, 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

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.

Examples