Wrapper for several IQRdataGENERAL cleaning functions

This functions performs several cleaning operations in the following order:

  • Set the BLLOQ handling method, using the function blloq_IQRdataGENERAL.

  • Set user defined records to IGNORE, using the function setIGNORErecords_IQRdataGENERAL.

  • Remove missing observation records with missing TIME, using the function rmMissingTIMEobsRecords_IQRdataGENERAL.

  • Set missing observation records with missing DV to IGNORE, using the function setMissingDVobsRecordsIGNORE_IQRdataGENERAL.

  • Remove user defined subjects, using the function rmSubjects_IQRdataGENERAL.

  • Remove non-dose and non-observation records, using the function rmNonTask_IQRdataGENERAL.

  • Remove placebo subjects, using the function rmPLACEBO_IQRdataGENERAL. The removal of placebo subjects is optional and can be changed by a input flag.

  • Removal of subjects without observations, using the function rmNOobsSUB_IQRdataGENERAL.

  • Removal of dose records with AMT=0, using the function rmAMT0_IQRdataGENERAL.

  • Removal of ignored record (MDV=1), using the function rmIGNOREd_IQRdataGENERAL.

  • Imputation of missing covariates, using the function covImpute_IQRdataGENERAL.

  • Removal of doses post last observation, using the function rmDosePostLastObs_IQRdataGENERAL. A path name can be provided. If it is, then protocols of the changes are written to log files in the IQRoutputTable format.

clean_IQRdataGENERAL(
  data,
  methodBLLOQ = "M1",
  records = NULL,
  subjects = NULL,
  FLAGrmPlacebo = FALSE,
  FLAGrmIGNOREDrecords = FALSE,
  FLAGrmDosePostLastObs = FALSE,
  continuousCovs = NULL,
  categoricalCovs = NULL,
  pathname = NULL
)

Arguments

data

IQRdataGENERAL object
methodBLLOQ

Character string, determining the method to use. Allowed are: "M1","M3","M4","M5","M6", and "M7". If NULL then not done.
records

List with reason for ignoring as fieldnames (in quotes) and value as a scalar or vector with the IXGDFs Example: records <- list("Outlier"=c(40,36,190), "Pre-first-dose-PK"=c(170,113,35))
subjects

List with reason for removal as fieldnames (in quotes) and value as a scalar or vector with the USUBJIDs Example: subjects <- list("Reason 1"=c("ZY800909106","ZY800909102"), "Reason 2"=c("ZY800505071"))
FLAGrmPlacebo

If TRUE then placebo subjects are removed from the data. If FALSE they are kept
FLAGrmIGNOREDrecords

If TRUE then MDV=1 records are removed. If FALSE then not removed.
FLAGrmDosePostLastObs

If TRUE then dose records post last observation in each individual are removed.
continuousCovs

In case covariate columns contain NA entries, this should by fixed by an imputation rule. There are two options:

  • Un-named vector: The vectors elements should contain the names of the covariate columns for which the imputation is done. In this case, each missing covariate is imputed to the median of this covariate (across subjects) in the dataset.

  • Named vector: The names of the vector elements should be the names of the covariate columns for which imputation is done. The values of the vector elements should be the values to which to impute the covariates to. Apart from numerical values also function names (such as "median" or "mean" can be provided).
categoricalCovs

In case covariate columns contain NA entries, this should by fixed by an imputation rule. For categorical covariates always a named vector needs to be provided. The names of the elements need to define the name of the covariate columns and the value of the numerical imputation value.
pathname

Pathname to where to write the log files

Value

"Cleaned" IQRdataGENERAL object

See also

Other IQRdataGeneral: +.IQRdataGENERAL(), IQRcalcTAD(), IQRdataGENERAL(), IQRexpandADDLII(), IQRloadCSVdata(), IQRsaveCSVdata(), addIndivRegressors_IQRdataGENERAL(), addLabel_IQRdataGENERAL(), attributes0(), blloqInfo_IQRdataGENERAL(), blloq_IQRdataGENERAL(), check_IQRdataGENERAL(), combine_IQRdataGENERAL(), convertCat2Text(), covImpute_IQRdataGENERAL(), date2dateday_IQRdataProgramming(), date2datetime_IQRdataProgramming(), date2time_IQRdataProgramming(), exportDEFINE_IQRaedataER(), exportDEFINE_IQRdataGENERAL(), exportDEFINEpdf_IQRdataGENERAL(), exportSYS_IQRdataGENERAL(), export_IQRdataGENERAL(), getLabels_dataframe(), getNAcolNLME_IQRdataGENERAL(), handleSameTimeObs_IQRdataGENERAL(), is_IQRdataGENERAL(), loadATRinfo_csvData(), loadAttributeFile(), load_IQRdataGENERAL(), mapCategoricalCovariate_IQRnlmeProject(), mapCategoricalCovariate_csvData(), mapContinuousCovariate_IQRnlmeProject(), mapContinuousCovariate_csvData(), mutateCov_IQRdataGENERAL(), obfuscate_IQRdataGENERAL(), plot.IQRdataGENERAL(), plotCorCat_IQRdataGENERAL(), plotCorCovCat_IQRdataGENERAL(), plotCorCov_IQRdataGENERAL(), plotCovDistribution_IQRdataGENERAL(), plotDoseSchedule_IQRdataGENERAL(), plotIndiv_IQRdataGENERAL(), plotRange_IQRdataGENERAL(), plotSampleSchedule_IQRdataGENERAL(), plotSpaghetti_IQRdataGENERAL(), print.IQRdataGENERAL(), removeCommata_dataframe(), rmAMT0_IQRdataGENERAL(), rmDosePostLastObs_IQRdataGENERAL(), rmIGNOREd_IQRdataGENERAL(), rmMissingTIMEobsRecords_IQRdataGENERAL(), rmNOobsSUB_IQRdataGENERAL(), rmNonTask_IQRdataGENERAL(), rmPLACEBO_IQRdataGENERAL(), rmSubjects_IQRdataGENERAL(), setIGNORErecords_IQRdataGENERAL(), setMissingDVobsRecordsIGNORE_IQRdataGENERAL(), subset.IQRdataGENERAL(), summary.IQRdataGENERAL(), summaryCat_IQRdataGENERAL(), summaryCov_IQRdataGENERAL(), summaryObservations_IQRdataGENERAL(), transformObs_IQRdataGENERAL(), unlabel_dataframe()

Other IQRdataGeneral: +.IQRdataGENERAL(), IQRcalcTAD(), IQRdataGENERAL(), IQRexpandADDLII(), IQRloadCSVdata(), IQRsaveCSVdata(), addIndivRegressors_IQRdataGENERAL(), addLabel_IQRdataGENERAL(), attributes0(), blloqInfo_IQRdataGENERAL(), blloq_IQRdataGENERAL(), check_IQRdataGENERAL(), combine_IQRdataGENERAL(), convertCat2Text(), covImpute_IQRdataGENERAL(), date2dateday_IQRdataProgramming(), date2datetime_IQRdataProgramming(), date2time_IQRdataProgramming(), exportDEFINE_IQRaedataER(), exportDEFINE_IQRdataGENERAL(), exportDEFINEpdf_IQRdataGENERAL(), exportSYS_IQRdataGENERAL(), export_IQRdataGENERAL(), getLabels_dataframe(), getNAcolNLME_IQRdataGENERAL(), handleSameTimeObs_IQRdataGENERAL(), is_IQRdataGENERAL(), loadATRinfo_csvData(), loadAttributeFile(), load_IQRdataGENERAL(), mapCategoricalCovariate_IQRnlmeProject(), mapCategoricalCovariate_csvData(), mapContinuousCovariate_IQRnlmeProject(), mapContinuousCovariate_csvData(), mutateCov_IQRdataGENERAL(), obfuscate_IQRdataGENERAL(), plot.IQRdataGENERAL(), plotCorCat_IQRdataGENERAL(), plotCorCovCat_IQRdataGENERAL(), plotCorCov_IQRdataGENERAL(), plotCovDistribution_IQRdataGENERAL(), plotDoseSchedule_IQRdataGENERAL(), plotIndiv_IQRdataGENERAL(), plotRange_IQRdataGENERAL(), plotSampleSchedule_IQRdataGENERAL(), plotSpaghetti_IQRdataGENERAL(), print.IQRdataGENERAL(), removeCommata_dataframe(), rmAMT0_IQRdataGENERAL(), rmDosePostLastObs_IQRdataGENERAL(), rmIGNOREd_IQRdataGENERAL(), rmMissingTIMEobsRecords_IQRdataGENERAL(), rmNOobsSUB_IQRdataGENERAL(), rmNonTask_IQRdataGENERAL(), rmPLACEBO_IQRdataGENERAL(), rmSubjects_IQRdataGENERAL(), setIGNORErecords_IQRdataGENERAL(), setMissingDVobsRecordsIGNORE_IQRdataGENERAL(), subset.IQRdataGENERAL(), summary.IQRdataGENERAL(), summaryCat_IQRdataGENERAL(), summaryCov_IQRdataGENERAL(), summaryObservations_IQRdataGENERAL(), transformObs_IQRdataGENERAL(), unlabel_dataframe()