ncappc: Performs NCA calculations and population PK model diagnosis.
In ncappc: NCA Calculations and Population Model Diagnosis

Description Usage Arguments Details Value Examples

ncappc is a flexible tool, to

perform a traditional NCA
perform simulation-based posterior predictive checks for a population PK model using NCA metrics.

ncappc(obsFile = "nca_original.npctab.dta",
  simFile = "nca_simulation.1.npctab.dta.zip", str1Nm = NULL,
  str1 = NULL, str2Nm = NULL, str2 = NULL, str3Nm = NULL,
  str3 = NULL, concUnit = NULL, timeUnit = NULL, doseUnit = NULL,
  obsLog = FALSE, simLog = obsLog, psnOut = TRUE, idNmObs = "ID",
  timeNmObs = "TIME", concNmObs = "DV", idNmSim = idNmObs,
  timeNmSim = timeNmObs, concNmSim = concNmObs, onlyNCA = FALSE,
  AUCTimeRange = NULL, backExtrp = FALSE, LambdaTimeRange = NULL,
  LambdaExclude = NULL, doseAmtNm = NULL,
  adminType = "extravascular", doseType = "ns", doseTime = NULL,
  Tau = NULL, TI = NULL, method = "linearup-logdown", blqNm = NULL,
  blqExcl = 1, evid = TRUE, evidIncl = 0, mdv = FALSE,
  filterNm = NULL, filterExcl = NULL, negConcExcl = FALSE,
  param = c("AUClast", "Cmax"), timeFormat = "number",
  dateColNm = NULL, dateFormat = NULL, spread = "npi",
  tabCol = c("AUClast", "Cmax", "Tmax", "AUCINF_obs", "Vz_obs", "Cl_obs",
  "HL_Lambda_z"), figFormat = "tiff", noPlot = FALSE,
  printOut = TRUE, studyName = NULL, new_data_method = TRUE,
  overwrite_SIMDATA = NULL, overwrite_sim_est_file = NULL,
  outFileNm = NULL, out_format = "html", gg_theme = theme_bw(),
  parallel = FALSE, extrapolate = FALSE, timing = FALSE, ...)

`obsFile`	Observed concentration-time data from an internal data frame or an external table with comma, tab or space as separators.
`simFile`	NONMEM simulation output with the simulated concentration-time data from an internal data frame or an external table. `NULL` produces just the NCA output, a filename or data frame produces the NCA output as well as the PopPK diagnosis. If `new_data_method=TRUE` then this can be a compressed file as well.
`str1Nm`	Column name for 1st level population stratifier. Default is `NULL`
`str1`	Stratification ID of the members within 1st level stratification (e.g c(1,2)). Default is `NULL`
`str2Nm`	Column name for 2nd level population stratifier. Default is `NULL`
`str2`	Stratification ID of the members within 2nd level stratification (e.g c(1,2)). Default is `NULL`
`str3Nm`	Column name for 3rd level population stratifier. Default is `NULL`
`str3`	Stratification ID of the members within 3rd level stratification (e.g c(1,2)). Default is `NULL`
`concUnit`	Unit of concentration (e.g. "ng/mL"). Default is `NULL`
`timeUnit`	Unit of time (e.g. "h"). Default is `NULL`
`doseUnit`	Unit of dose amount (e.g. "ng"). Default is `NULL`
`obsLog`	If `TRUE` concentration in observed data is in logarithmic scale. Default is `FALSE`
`simLog`	If `TRUE` concentration in simulated data is in logarithmic scale. Default is `FALSE`
`psnOut`	If `TRUE` observed data is an output from PsN or in NONMEM output format. Default is `TRUE`
`idNmObs`	Column name for ID in observed data. Default is "ID"
`timeNmObs`	Column name for time in observed data. Default is "TIME"
`concNmObs`	Column name for concentration in observed data. Default is "DV"
`idNmSim`	Column name for ID in simulated data. Default is "ID"
`timeNmSim`	Column name for time in simulated data. Default is "TIME"
`concNmSim`	Column name for concentration in simulated data. Default is "DV"
`onlyNCA`	If `TRUE` only NCA is performed and ppc part is ignored although simFile is not `NULL`. Default is `FALSE`
`AUCTimeRange`	User-defined window of time used to estimate AUC. Default is `NULL`
`backExtrp`	If `TRUE` back-extrapolation is performed while estimating AUC. Default is `FALSE`
`LambdaTimeRange`	User-defined window of time to estimate elimination rate-constant. This argument lets the user to choose a specific window of time to be used to estimate the elimination rate constant (Lambda) in the elimination phase. The accepted format for the input to this argument is a numeric array of two elements; `c(14,24)` will estimate the Lambda using the data within the time units 14 to 24. Default is `NULL`
`LambdaExclude`	User-defined excluded observation time points for estimation of Lambda. This can be numeric value or logical condition (e.g. c(1, 2, "<20", ">=100", "!=100")). Default is `NULL`
`doseAmtNm`	Column name to specify dose amount. Default is `NULL`
`adminType`	Route of administration. Allowed options are iv-bolus, iv-infusion or extravascular. Default is "extravascular"
`doseType`	Steady-state (ss) or non-steady-state (ns) dose. Default is "ns"
`doseTime`	Dose time prior to the first observation for steady-state data. Default is `NULL`
`Tau`	Dosing interval for steady-state data. Default is `NULL`
`TI`	Infusion duration. If TI is a single numeric value, TI is the same for all individuals. If TI is the name of a column with numeric data present in the data set, TI is set to the unique value of the column for a given individual. Default is `NULL`
`method`	Method to estimate AUC. `linear` method applies the linear trapezoidal rule to estimate the area under the curve. `log` method applies the logarithmic trapezoidal rule to estimate the area under the curve. `linearup-logdown` method applies the linear trapezoidal rule to estimate the area under the curve for the ascending part of the curve and the logarithmic trapezoidal rule to estimate the area under the curve for the descending part of the curve. Default is "linearup-logdown"
`blqNm`	Name of BLQ column if used to exclude data. Default is `NULL`
`blqExcl`	Excluded BLQ value; either a numeric value or a logical condition (e.g. 1 or ">=1" or c(1,">3")). Used only if the `blqNm` is not `NULL`. Default is "1"
`evid`	If `TRUE` EVID is used to filter data. Default is `TRUE`
`evidIncl`	Included values in EVID. Default is "0"
`mdv`	If `TRUE` MDV is used to include data when MDV=0. Default is `FALSE`
`filterNm`	Column name to filter data. Default is `NULL`
`filterExcl`	Row exclusion criteria based on the column defined by `filterNm`. This can be numeric value or logical condition (e.g. c(1, 2, "<20", ">=100", "!=100")). Default is `NULL`
`negConcExcl`	If `TRUE` negative concentrations are excluded. Default is `FALSE`
`param`	NCA parameters (AUClast, AUClower_upper, AUCINF_obs, AUCINF_pred, AUMClast, Cmax, Tmax, HL_Lambda_z). Default is (c"AUClast", "Cmax")
`timeFormat`	time format (number, H:M, H:M:S). Default is "number"
`dateColNm`	column name for date if used (e.g. "Date", "DATE"). Default is `NULL`
`dateFormat`	date format (D-M-Y, D/M/Y or any other combination of D,M,Y). Default is `NULL`
`spread`	Measure of the spread of simulated data (`"ppi"` (95% parametric prediction interval) or `"npi"` (95% nonparametric prediction interval)). Default is "npi"
`tabCol`	Output columns to be printed in the report in addition to ID, dose and population strata information (list of NCA metrics in a string array). Default is c("AUClast", "Cmax", "Tmax", "AUCINF_obs", "Vz_obs", "Cl_obs", "HL_Lambda_z")
`figFormat`	format of the produced figures (bmp, jpeg, tiff, png). Default is "tiff"
`noPlot`	If `TRUE` only NCA calculations are performed without any plot generation. Default is `FALSE`
`printOut`	If `TRUE` tabular and graphical outputs are saved on the disk. Default is `TRUE`
`studyName`	Name of the study to be added as a description in the report. Default is `NULL`
`new_data_method`	If `TRUE` a faster method of reading data is tested. Default is `TRUE`
`overwrite_SIMDATA`	If `TRUE` new information is created in the SIMDATA directory. If `FALSE` the information in the SIMDATA directory is used. If `NULL` a dialog will come up to ask the user what to do. Default is `NULL`
`overwrite_sim_est_file`	If `TRUE` The NCA metrics are created again based on the simulation data. If `FALSE` the information in the ncaSimEst file is used. If `NULL` a dialog will come up to ask the user what to do. Default is `NULL`
`outFileNm`	Additional tag to the name of the output html and pdf output file hyphenated to the standard ncappc report file name standard ncappc report file name. Default is `NULL`
`out_format`	What type of output format should the NCA report have? Pass "all" to render all formats defined within the rmarkdown file. Pass "first" to render the first format defined within the rmarkdown file. Pass "html" to render in HTML. Pass "pdf" to render in PDF.
`gg_theme`	Which ggplot theme should be used for the plots?
`parallel`	Should the nca computations for the simulated data be run in parallel? See `start_parallel` for a description and additional arguments that can be added to this function and passed to `start_parallel`.
`extrapolate`	Should the NCA calculations extrapolate from the last observation to infinity?
`timing`	Should timings of calculations be reported to the screen?
`...`	Additional arguments passed to other functions, including `start_parallel`.

Non-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration-time curve and peak concentration. ncappc performs a traditional NCA using the observed plasma concentration-time data. In the presence of simulated plasma concentration-time data, ncappc also performs simulation-based posterior predictive checks (ppc) using NCA metrics for the corresponding population PK (PopPK) model used to generate the simulated data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. Additionally, ncappc reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. ncappc produces two default outputs depending on the type of analysis performed, i.e., traditional NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 7 sets of graphical outputs to assess the ability of a population model to simulate the concentration-time profile of a drug and thereby identify model misspecification. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. The default values of the arguments used in ncappc are shown in the Usage section of this document and/or in bold in the Arguments section.

NCA results and diagnostic test results

out <- ncappc(obsFile=system.file("extdata","pkdata.csv",package="ncappc"), 
  onlyNCA = TRUE,
  extrapolate = TRUE,
  printOut = FALSE,
  evid = FALSE,
  psnOut=FALSE)
  

data_1 <- data.frame(
  ID=1,
  TIME = c(0,0.25,0.5,1,1.5,2,3,4,6,8,12,16,24),
  DV=c(0, 0.07, 0.14, 0.21, 0.24, 0.27, 0.26, 0.25, 0.22, 0.19, 0.13, 0.081, 0.033)
)
out_1 <- ncappc(obsFile=data_1,
                onlyNCA = TRUE,
                extrapolate = TRUE,
                printOut = FALSE,
                evid = FALSE,
                timing=TRUE)


data_2 <- dplyr::filter(data_1,TIME>17|TIME<3)
out_2 <- ncappc(obsFile=data_2,
                onlyNCA = TRUE,
                extrapolate = TRUE,
                printOut = FALSE,
                evid = FALSE,
                force_extrapolate=TRUE)