setNCASettings
[PKanalix] Set the value of one or several of the settings associated to the non compartmental analysis
Set the value of one or several of the settings associated to the non compartmental analysis. Associated settings are:
| "obsidtouse" | (character) | The observation id from data section to use for computations. |
| "administrationType" | (list) | |
| list(key = "admId", value = character("intravenous" or "extravascular")). admId Admninistration ID from data set or 1 if no admId column in the dataset. | "integralMethod" | (character) |
| Method for AUC and AUMC calculation and interpolation. Possible methods are "linTrapLinInterp" (default, linear trapezoidal linear), "linLogTrapLinLogInterp" (linear log trapezoidal), "upDownTrapUpDownInterp" (linear up log down ) and "linTrapLinLogInterp" (linear trapezoidal linear/log). | "partialAucTime" | |
| (list) | The first element of the list is a logical describing if this setting is used. The second element of the list is a list of the values of the bounds of the partial AUC calculation intervals. By default, the logical equals FALSE, the lower bound equals to the last dose time in the data set and the upper bound equals to the last observation time of the selected observation ID. | |
| "interdoseIntervalForSingleDose" | (list) | The first element of the list is a logical describing if this setting is used. The second element of the list is a number defining the interdose interval. By default, the logical equals FALSE. |
| "blqMethodBeforeTmax" | (character) | |
| Method by which the BLQ data before Tmax should be replaced. Possible methods are "missing", "LOQ", "LOQ2" or "zero" (default). | "blqMethodAfterTmax" | |
| (character) | Method by which the BLQ data after Tmax should be replaced. Possible methods are "zero", "missing", "LOQ" or "LOQ2" (default). | |
| "ajdr2AcceptanceCriteria" | (list) | The first element of the list is a logical describing if this setting is used. The second element of the list is the value of the adjusted R2 acceptance criteria for the estimation of lambda_Z. By default, the logical equals FALSE and the value is 0.98. |
| "extrapAucAcceptanceCriteria" | (list) | |
| The first element of the list is a logical describing if this setting is used. The second element of the list is the value of the AUC extrapolation acceptance criteria for the estimation of lambda_Z. By default, the logical equals FALSE and the value is 20. | "spanAcceptanceCriteria" | |
| (list) | The first element of the list is a logical describing if this setting is used. The second element of the list is the value of the span acceptance criteria for the estimation of lambda_Z. By default, the logical equals FALSE and the value is 3. | |
| "lambdaRule" | (character) | Main rule for the lambda_Z estimation. Possible rules are "R2", "interval", "points" or "adjustedR2" (default). |
| "timeInterval" | (vector) | |
| Time interval for the lambda_Z estimation when "lambdaRule" = "interval". This is a vector of size two, default = c(-Inf, Inf) | "timeValuesPerId" | |
| (list) | list("idName" = idTimes,...): idTimes Observation times to use for the calculation of lambda_Z for the id idName. Default = NULL, all the times values are used. | |
| "nbPoints" | (integer) | Number of points for the lambda_Z estimation when "lambdaRule" = "points". Default = 3. |
| "maxNbOfPoints" | (list) | |
| The first element of the list is a logical describing if this setting is used. The second element of the list is the value maximum number of points to use for the lambda_Z estimation when "lambdaRule" = "R2" or "adjustedR2". By default, the logical equals FALSE and the value is 500. | "startTimeNotBefore" | |
| (list) | The first element of the list is a logical describing if this setting is used. The second element of the list is the value minimum time value to use for the lambda_Z estimation when "lambdaRule" = "R2" or "adjustedR2". By default, the logical equals FALSE and the value is 0. | |
| "weightingNca" | (character) | Weighting method used for the regression that estimates lambda_Z. Possible methods are "Y", "Y2" or "uniform" (default). |
| "computedNCAParameters" | (vector) | |
| All the parameters to compute during the analysis. Possible parameters: | ||
| ||
| "sparse" | (logical) | If the data should be considered sparse, and hence NCA should run on the mean profiles. By default, the logical equals FALSE. |
| "sparseStratification" | (vector) | |
| The covariates that should be used to stratify the mean profiles for sparse NCA. By default, the list is empty. | "sparseCensoring" | |
| (list) | The censoring settings to calculate the mean profiles for sparse NCA in case of censored data. It is a list with: | |
|
Usage
setNCASettings(...)Arguments
- ...
A collection of comma-separated pairs {settingName = settingValue}.
See also
Examples
if (FALSE) {
setNCASettings(integralMethod = "LinLogTrapLinLogInterp", weightingnca = "uniform") # set the settings whose name has been passed in argument
setNCASettings(administrationType = list("1"="extravascular")) # set the administration id "1" to extravascular
setNCASettings(startTimeNotBefore = list(TRUE, 15)) # set the estimation of the lambda_z with points with time over 15
setNCASettings(timeValuesPerId = list('1'=c(4, 6, 8, 30), '4'=c(8, 12, 18, 24, 30))) # set the points to use for the lambda_z to time={4, 6, 8, 30} for id '1' and ime={8, 12, 18, 24, 30} for id '4'
setNCASettings(timeValuesPerId = NULL) # set the points to use for the lambda_z to the default rule
setNCASettings(sparse=TRUE, sparseStratification=list("DGROUP", "STUDY"), sparseCensoring=list(rule = list(criterion = "N_BLQ >= 4", target = "individualValues", rules = c("missing", "loq")))) # set sparse NCA with stratication by STUDY and DGROUP. At each time point and in each group, if there are less than 4 censored samples they are considered as missing (ignored), otherwise the mean value is replaced by the largest LOQ.
setNCASettings(sparse=T, sparseCensoring=list(rule = "zero", checkMeanSamples = list(loq = 3500, blqBeforeTmax = "zero", blqAfterTmax = "missing"))) # set sparse NCA. At each time point, censored samples are replaced by zero to calculate mean samples. If calculated mean samples are smaller than 3500 they are replaced by zero before Tmax and ignored after Tmax.
}