Guided-demo: Exploring 'ddmore' R package functionality with the warfarin model

UseCase8 : Implemententation of between occasion variability to describe warfarine pharmacokinetics

This example script is intended to illustrate how to use the 'ddmore' R package to perform a M&S workflow using the DDMoRe Standalone Execution Environment (SEE).

The following steps are implemented in this workflow:

To run a task, select wiht the coursor any code lines you wish to execute and press CTRL+R+R in your keyboard. An HTML file containing the commands in this file and associated output will be provided to allow the user to compare the results

Initialisation

Clear workspace and set working directory under 'UsesCasesDemo' project

rm(list=ls(all=F))
mydir <- file.path(Sys.getenv("MDLIDE_WORKSPACE_HOME"),"UseCasesDemo")
setwd(mydir)

Create a working directory under 'models' folder where results are stored

uc<-"UseCase8"
datafile <- "warfarin_conc_bov_P4_sort.csv"
mdlfile <- paste0(uc,".mdl")

Create a new folder to be used as working directory and where results will be stored

wd <- file.path(mydir,uc)
dir.create(wd)

Copy the dataset and the .mdl file available under “models” into the working directory

file.copy(file.path(mydir,"models", datafile),wd)
## [1] TRUE
file.copy(file.path(mydir,"models",mdlfile),wd)
## [1] TRUE

Set the working directory.

setwd(file.path(mydir,uc))

The working directory needs to be set differently when knitr R package is used to spin the file

library(knitr)
opts_knit$set(root.dir = file.path(mydir,uc)) 

List files available in working directory

list.files()
## [1] "UseCase8.mdl"                  "warfarin_conc_bov_P4_sort.csv"

Introduction to 'ddmore' R package

View objects within the .mdl file

Use 'ddmore' function getMDLObjects() to retrieve model object(s) from an existing .mdl file. This function reads the MDL in an .mdl file and parses the MDL code for each MDL Object into an R list of objects of appropriate types with names corresponding to the MDL Object names given in the file.

myMDLObj <- getMDLObjects(mdlfile)
length(myMDLObj)
## [1] 4
names(myMDLObj)
## [1] "warfarin_PK_BOV_dat"  "warfarin_PK_BOV_par"  "warfarin_PK_BOV_mdl" 
## [4] "warfarin_PK_BOV_task"

Use 'ddmore' function getDataObjects() to retrieve only data object(s) from an existing .mdl file This function returns a list of Parameter Object(s) from which we select the first element Hover over the variable name to see its structure

myDataObj <- getDataObjects(mdlfile)[[1]]

Use 'ddmore' function getParameterObjects() to retrieve only parameter object(s) from an existing .mdl file

myParObj <- getParameterObjects(mdlfile)[[1]]

Use 'ddmore' function getModelObjects() to retrieve only model object(s) from an existing .mdl file.

myModObj <- getModelObjects(mdlfile)[[1]]

Use 'ddmore' function getTaskPropertiesObjects() to retrieve only task properties object(s) from an existing .mdl file

myTaskObj <- getTaskPropertiesObjects(mdlfile)[[1]]

Exploratory Data Analysis

Recall that getDataObjects only reads the MDL code from the .mdl file. Use 'ddmore' function readDataObj() to create an R object from the MDL data object.

myData <- readDataObj(myDataObj)

Let's look at the first 6 lines of the data set

head(myData)
##   ID TIME   WT AGE    SEX AMT OCC       DV MDV
## 1  1  0.0 66.7  50 female 100   1  0.00000   1
## 2  1  0.5 66.7  50 female   0   1 -0.23526   0
## 3  1  1.0 66.7  50 female   0   1  5.68580   0
## 4  1  2.0 66.7  50 female   0   1 14.87200   0
## 5  1  3.0 66.7  50 female   0   1 12.81900   0
## 6  1  6.0 66.7  50 female   0   1 12.75200   0

Extract only observation records

myEDAData<-myData[myData$MDV==0,]

Open an R window to record and access all your plots

windows(record=TRUE) 

Now plot the data using xyplot from the lattice library

plot1 <- xyplot(DV~TIME|OCC,groups=ID,data=myEDAData,type="b",ylab="Conc. (mg/L)",xlab="Time (h)",scales=list(relation="free"))
print(plot1)

plot of chunk unnamed-chunk-15

plot2 <- xyplot(DV~TIME|ID,data=myEDAData,type="b",layout=c(3,4),ylab="Conc. (mg/L)",xlab="Time (h)",scales=list(relation="free"))
print(plot2)

plot of chunk unnamed-chunk-15 plot of chunk unnamed-chunk-15 plot of chunk unnamed-chunk-15

Export results to PDF file

pdf(paste0(uc,"_EGA.pdf"))
 print(plot1)
 print(plot2)
dev.off()
## rj.GD 
##     2

Model Development

ESTIMATE model parameters using Monolix

BOV is currently not implemented in Monolix

#mlx <- estimate(mdlfile, target="MONOLIX", subfolder="Monolix")
#slotNames(mlx)
# #' The ddmore "LoadSOObj" reads and parsed existing Standardise Output Objects
# #'  mlx <- LoadSOObject("Monolix/UseCase8.SO.xml")

# #' The ddmore function "getPopulationParameters" extracts the Population Parameter values from 
# #' an R object of (S4) class "StandardOutputObject" and returns the estimates. 
# #' See documentation for getPopulationParameters to see other arguments and settings for this function.  
#parameters_mlx <- getPopulationParameters(mlx, what="estimates")
#print(parameters_mlx)
#print(getPopulationParameters(mlx, what="precisions"))

#print(getEstimationInfo(mlx))


# #' Perform model diagnostics for the base model using Xpose functions (graphs are exported to PDF)
# #' -------------------------
# #' Use 'ddmore' function as.xpdb() to create an Xpose database object from
# #' the Standardised Output object, regardless of target software used for estimation.
#mlx.xpdb<-as.xpdb(mlx,datafile)

# #' We can then call Xpose functions referencing this mlx.xpdb object as the input. 
# #' Perform some basic goodness of fit
#print(basic.gof(mlx.xpdb))
#print(ind.plots(mlx.xpdb))

# #' Export results to PDF file
#pdf("GOF_MLX.pdf")
#print(basic.gof(mlx.xpdb))
#print(ind.plots(mlx.xpdb))
#dev.off()

SAEM Estimation with NONMEM

NM <- estimate(mdlfile, target="NONMEM", subfolder="NONMEM")
## -- Fri Dec 11 11:45:58 2015
## New
## Submitted
## Job 27c11773-b2fb-497e-8043-1c25b17d5113 progress:
## Running [ .......................................................................................................................................................... ]
## Importing Results
## Copying the result data back to the local machine for job ID 27c11773-b2fb-497e-8043-1c25b17d5113...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd0383366be to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase8/NONMEM
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Estimation:PopulationEstimates
##       Estimation:IndividualEstimates
##       Estimation:Residuals
##       Estimation:Predictions
##       Estimation:Likelihood
## 
## The following MESSAGEs were raised during the job execution:
##  minimization_successful: 1
##  covariance_step_run: 0
##  rounding_errors: 0
##  estimate_near_boundary: 0
##  s_matrix_singular: 0
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 12:37:30 2015

The ddmore “LoadSOObj” reads and parsed existing Standardise Output Objects NM <- LoadSOObject(“NONMEM/UseCase8.SO.xml”) The ddmore function “getPopulationParameters” extracts the Population Parameter values from an R object of (S4) class “StandardOutputObject” and returns the estimates. See documentation for getPopulationParameters to see other arguments and settings for this function.

parameters_nm <- getPopulationParameters(NM, what="estimates")
print(parameters_nm)
## $MLE
##               POP_CL                POP_V               POP_KA 
##           0.10645300           8.51583000           2.02428000 
##             POP_TLAG             RUV_PROP              RUV_ADD 
##           1.11572000           0.16431600           0.11582900 
##               BSV_CL                BSV_V               BSV_KA 
##           0.01264640           0.00501537           0.68285100 
##               BOV_CL ETA_BOV_CL_ETA_BOV_V                BOV_V 
##           0.14165300          -0.00151364           0.18399900 
##           BETA_CL_WT            BETA_V_WT             BSV_TLAG 
##           0.75000000           1.00000000           0.01000000
#print(parameters_mlx)

Covariance step cannot be requested in the current version of the Framework

#print(getPopulationParameters(NM, what="precisions"))

print(getEstimationInfo(NM))
## $Likelihood
## $Likelihood$Deviance
## [1] 108.8807
## 
## 
## $Messages
## $Messages$Info
## $Messages$Info$minimization_successful
## [1] "1"
## 
## $Messages$Info$covariance_step_run
## [1] "0"
## 
## $Messages$Info$rounding_errors
## [1] "0"
## 
## $Messages$Info$estimate_near_boundary
## [1] "0"
## 
## $Messages$Info$s_matrix_singular
## [1] "0"
## 
## $Messages$Info$nmoutput2so_version
## [1] "This SOBlock was created with nmoutput2so version 4.5.11"

Xpose diagnostics using NONMEM output

Use 'ddmore' function as.xpdb() to create an Xpose database object from the Standardised Output object, regardless of target software used for estimation.

nm.xpdb<-as.xpdb(NM,datafile)
## 
## Removed dose rows in rawData+Predictions slot of SO to enable merge with Residuals data.
## 
## Residuals data does not currently contain dose rows in output from Nonmem executions.
## Warning in mergeCheckColumnNames(df1, df2, ID.colName = ID.colName, TIME.colName = TIME.colName): The following duplicate column names were detected and will be dropped from the output: 
##       OCC
## Warning in mergeCheckColumnNames(df1, df2, ID.colName = ID.colName, TIME.colName = TIME.colName): The following duplicate column names were detected and will be dropped from the output: 
##       OCC

We can then call Xpose functions referencing this mlx.xpdb object as the input. Perform some basic goodness of fit

print(basic.gof(nm.xpdb))

plot of chunk unnamed-chunk-21

print(basic.gof(nm.xpdb, by="OCC"))

plot of chunk unnamed-chunk-21

print(ind.plots(nm.xpdb))

plot of chunk unnamed-chunk-21 plot of chunk unnamed-chunk-21

print(parm.hist(nm.xpdb))

plot of chunk unnamed-chunk-21

Export results to a PDF file

pdf("GOF_NM.pdf")
 print(basic.gof(nm.xpdb))
 print(basic.gof(nm.xpdb, by="OCC"))
 print(ind.plots(nm.xpdb))
 print(parm.hist(nm.xpdb))
dev.off()
## rj.GD 
##     2

Change estimation method to FOCEI (for speed)

MDL Objects can be manipulated from R to change for example the estimation algorithm

myTaskProperties <- getTaskPropertiesObjects(mdlfile)[[1]]
myNewTaskProperties <- myTaskProperties
myNewTaskProperties@ESTIMATE$algo <- "focei"

Assembling the new MOG. Note that we reuse the data and model from the previous run.

myNewerMOG <- createMogObj(dataObj = getDataObjects(mdlfile)[[1]], 
        parObj = getParameterObjects(mdlfile)[[1]], 
        mdlObj = getModelObjects(mdlfile)[[1]], 
        taskObj = myNewTaskProperties)

We can then write the MOG back out to an .mdl file.

mdlfile.FOCEI <- paste0(uc,"_FOCEI.mdl")
writeMogObj(myNewerMOG,mdlfile.FOCEI)

Test estimation using this new MOG.

NM.FOCEI <- estimate(mdlfile.FOCEI, target="PsN", subfolder="NONMEM_FOCEI")
## -- Fri Dec 11 12:38:09 2015
## New
## Submitted
## Job 4ef718ac-10b7-4fa9-af66-91e135abbe4b progress:
## Running [ ..................... ]
## Importing Results
## Copying the result data back to the local machine for job ID 4ef718ac-10b7-4fa9-af66-91e135abbe4b...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd01a1a47d6 to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase8/NONMEM_FOCEI
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Estimation:PopulationEstimates
##       Estimation:IndividualEstimates
##       Estimation:Residuals
##       Estimation:Predictions
##       Estimation:Likelihood
## 
## The following WARNINGs were raised during the job execution:
##  minimization_successful: 0
##  rounding_errors: 1
##  hessian_reset: 1
##  zero_gradients: 4
##  final_zero_gradients: 1
##  estimate_near_boundary: 1
## 
## The following MESSAGEs were raised during the job execution:
##  covariance_step_run: 0
##  s_matrix_singular: 0
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 12:45:16 2015

Load previous results NM.FOCEI <- LoadSOObject(“NONMEM_FOCEI/UseCase8_FOCEI.SO.xml”) Results from NONMEM should be comparable to previous results

print(getPopulationParameters(NM.FOCEI,  what="estimates"))
## $MLE
##               POP_CL                POP_V               POP_KA 
##          0.108537000          8.567980000          1.864140000 
##             POP_TLAG             RUV_PROP              RUV_ADD 
##          0.986996000          0.166706000          0.143506000 
##               BSV_CL                BSV_V               BSV_KA 
##          0.000109351          0.000010000          1.381770000 
##               BOV_CL ETA_BOV_CL_ETA_BOV_V                BOV_V 
##          0.142930000          0.005754720          0.179456000 
##           BETA_CL_WT            BETA_V_WT             BSV_TLAG 
##          0.750000000          1.000000000          0.010000000
print(parameters_nm)
## $MLE
##               POP_CL                POP_V               POP_KA 
##           0.10645300           8.51583000           2.02428000 
##             POP_TLAG             RUV_PROP              RUV_ADD 
##           1.11572000           0.16431600           0.11582900 
##               BSV_CL                BSV_V               BSV_KA 
##           0.01264640           0.00501537           0.68285100 
##               BOV_CL ETA_BOV_CL_ETA_BOV_V                BOV_V 
##           0.14165300          -0.00151364           0.18399900 
##           BETA_CL_WT            BETA_V_WT             BSV_TLAG 
##           0.75000000           1.00000000           0.01000000
#print(parameters_mlx)

Xpose diagnostics using NONMEM output

nmfocei.xpdb<-as.xpdb(NM.FOCEI,datafile)
## 
## Removed dose rows in rawData+Predictions slot of SO to enable merge with Residuals data.
## 
## Residuals data does not currently contain dose rows in output from Nonmem executions.
## Warning in mergeCheckColumnNames(df1, df2, ID.colName = ID.colName, TIME.colName = TIME.colName): The following duplicate column names were detected and will be dropped from the output: 
##       OCC
## Warning in mergeCheckColumnNames(df1, df2, ID.colName = ID.colName, TIME.colName = TIME.colName): The following duplicate column names were detected and will be dropped from the output: 
##       OCC

Basic diagnostics for NONMEM fit.

print(basic.gof(nmfocei.xpdb))

plot of chunk unnamed-chunk-27

Export graphs to a PDF file

pdf("GOF_NM_FOCEI.pdf")
print(basic.gof(nmfocei.xpdb))
dev.off()
## rj.GD 
##     2

Run the bootstrap using PsN

The ddmore “bootstrap.PsN” function is a wrap up function that calls Bootstrap PsN functionality using as input an MDL file that will be translated to NMTRAN as first step. Additional PsN arguments can be specified under the “bootstrapOptions” attribute. After task execution, the output from PsN is converted to a Standardised Output object which is saved in a .SO.xml file. Translated files and PsN output will be returned in the ./Bootstrap subfolder

bootstrapResults <- bootstrap.PsN(mdlfile.FOCEI, samples=20, seed=123456,
        bootstrapOptions=" -no-skip_minimization_terminated -threads=2",
        subfolder="Bootstrap", plot=TRUE)
## -- Fri Dec 11 12:45:34 2015
## New
## Submitted
## Job 1e49bd09-4b15-4a3e-9522-5ee91435cf59 progress:
## Running [ .................................................................................................................................................................................................................................. ]
## Importing Results
## Copying the result data back to the local machine for job ID 1e49bd09-4b15-4a3e-9522-5ee91435cf59...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd043d87035 to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase8/Bootstrap
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Estimation:PopulationEstimates
##       Estimation:PrecisionPopulationEstimates
##       Estimation:IndividualEstimates
##       Estimation:Residuals
##       Estimation:Predictions
##       Estimation:Likelihood
## 
## The following WARNINGs were raised during the job execution:
##  minimization_successful: 0
##  rounding_errors: 1
##  hessian_reset: 1
##  zero_gradients: 4
##  final_zero_gradients: 1
##  estimate_near_boundary: 1
## 
## The following MESSAGEs were raised during the job execution:
##  covariance_step_run: 0
##  s_matrix_singular: 0
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 14:01:06 2015
## Warning: NAs introduced by coercion
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plot of chunk Bootstrap

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plot of chunk Bootstrap

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Load results from a bootstrap previously performed bootstrapResults <- LoadSOObject(“Bootstrap/UseCase8_FOCEI.SO.xml”) Export bootstrap histograms to a pdf

pdf(paste0(uc,"_Bootstrap.pdf"))
print(boot.hist(results.file = file.path("Bootstrap",paste0("raw_results_",uc,"_FOCEI.csv")),
                incl.ids.file = file.path("Bootstrap","included_individuals1.csv")))
## [[1]]
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dev.off()
## rj.GD 
##     2

Extract parameter estimates and precision from bootstrap results.

print(getPopulationParameters(bootstrapResults, what="estimates"))
## $MLE
##               POP_CL                POP_V               POP_KA 
##          0.108537000          8.567980000          1.864140000 
##             POP_TLAG             RUV_PROP              RUV_ADD 
##          0.986996000          0.166706000          0.143506000 
##               BSV_CL                BSV_V               BSV_KA 
##          0.000109351          0.000010000          1.381770000 
##               BOV_CL ETA_BOV_CL_ETA_BOV_V                BOV_V 
##          0.142930000          0.005754720          0.179456000 
##           BETA_CL_WT            BETA_V_WT             BSV_TLAG 
##          0.750000000          1.000000000          0.010000000 
## 
## $Bootstrap
##                                 Parameter        Mean     Median
## POP_CL                             POP_CL 0.108972100 0.10917500
## POP_V                               POP_V 8.289335000 8.29457500
## POP_KA                             POP_KA 1.261541000 0.90741650
## POP_TLAG                         POP_TLAG 0.791186800 0.68343900
## RUV_PROP                         RUV_PROP 0.135851300 0.14389750
## RUV_ADD                           RUV_ADD 0.377978200 0.20015300
## BETA_CL_WT                     BETA_CL_WT 0.750000000 0.75000000
## BETA_V_WT                       BETA_V_WT 1.000000000 1.00000000
## BSV_CL                             BSV_CL 0.019327150 0.00781776
## BSV_V                               BSV_V 0.001758783 0.00001000
## BSV_KA                             BSV_KA 0.551202100 0.12361150
## BSV_TLAG                         BSV_TLAG 0.010000000 0.01000000
## BOV_CL                             BOV_CL 0.119919200 0.11808700
## ETA_BOV_CL_ETA_BOV_V ETA_BOV_CL_ETA_BOV_V 0.014670960 0.01614930
## BOV_V                               BOV_V 0.161833600 0.17080400

Extract the information regarding the precision intervals

print(getPopulationParameters(bootstrapResults, what="intervals")$Bootstrap)
##               Parameter        Mean     Median      Perc_5    Perc_95
## 1            BETA_CL_WT 0.750000000 0.75000000  0.75000000 0.75000000
## 2             BETA_V_WT 1.000000000 1.00000000  1.00000000 1.00000000
## 3                BOV_CL 0.119919200 0.11808700  0.07618342 0.16838330
## 4                 BOV_V 0.161833600 0.17080400  0.07509313 0.20881350
## 5                BSV_CL 0.019327150 0.00781776  0.00001000 0.06822299
## 6                BSV_KA 0.551202100 0.12361150  0.00001000 3.83086300
## 7              BSV_TLAG 0.010000000 0.01000000  0.01000000 0.01000000
## 8                 BSV_V 0.001758783 0.00001000  0.00001000 0.01760355
## 9  ETA_BOV_CL_ETA_BOV_V 0.014670960 0.01614930 -0.04222542 0.06084423
## 10               POP_CL 0.108972100 0.10917500  0.09432154 0.12040250
## 11               POP_KA 1.261541000 0.90741650  0.69019820 4.41032700
## 12             POP_TLAG 0.791186800 0.68343900  0.50005650 1.46952100
## 13                POP_V 8.289335000 8.29457500  7.49526200 9.12264900
## 14              RUV_ADD 0.377978200 0.20015300  0.09830445 0.93531790
## 15             RUV_PROP 0.135851300 0.14389750  0.06631803 0.20203260

VPC of model

Before running the VPC with PsN we must update the (initial) values in the MDL Parameter Object MLE estimates from previous step can be used

structuralPar <- getPopulationParameters(NM.FOCEI, what="estimates",block='structural')$MLE
variabilityPar <- getPopulationParameters(NM.FOCEI, what="estimates",block='variability')$MLE

In the current version of the SO standard, we need to manually update parameter names for correlation and covariance parameters to match the SO with the MDL. This will not be needed in future releases. The SO object has parameter BOV_CL_BOV_V. This needs to be renamed to conform to model Correlation name OMEGA

variabilityNames <- names(myParObj@VARIABILITY)
names(variabilityPar)[names(variabilityPar)=="ETA_BOV_CL_ETA_BOV_V"] <- grep("BOV_COV_CL_V",variabilityNames,value=T)
names(variabilityPar)
## [1] "BSV_CL"       "BSV_V"        "BSV_KA"       "BOV_CL"      
## [5] "BOV_COV_CL_V" "BOV_V"        "BSV_TLAG"

Update the parameter object using the ddmore “updateParObj” function. This function updates an R object of (S4) class “parObj”. The user chooses which block to update, what items within that block, and what to replace those items with. NOTE: that updateParObj can only update attributes which ALREADY EXIST in the MDL Parameter Object for that item. This ensures that valid MDL is preserved.

myParObj <- getParameterObjects(mdlfile)[[1]]
myParObjUpdated <- updateParObj(myParObj,block="STRUCTURAL",
        item=names(structuralPar),
        with=list(value=structuralPar))
myParObjUpdated <- updateParObj(myParObjUpdated,block="VARIABILITY",
        item=names(variabilityPar),
        with=list(value=variabilityPar))

Check that the appropriate initial values have been updated to the MLE values from the previous fit.

print(myParObjUpdated@STRUCTURAL)
## $POP_CL
## $POP_CL$value
## [1] "0.108537"
## 
## $POP_CL$lo
## [1] "0.001"
## 
## 
## $POP_V
## $POP_V$value
## [1] "8.56798"
## 
## $POP_V$lo
## [1] "0.001"
## 
## 
## $POP_KA
## $POP_KA$value
## [1] "1.86414"
## 
## $POP_KA$lo
## [1] "0.001"
## 
## 
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.986996"
## 
## $POP_TLAG$lo
## [1] "0.001"
## 
## 
## $BETA_CL_WT
## $BETA_CL_WT$value
## [1] "0.75"
## 
## $BETA_CL_WT$fix
## [1] "true"
## 
## 
## $BETA_V_WT
## $BETA_V_WT$value
## [1] "1"
## 
## $BETA_V_WT$fix
## [1] "true"
## 
## 
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.166706"
## 
## $RUV_PROP$lo
## [1] "0"
## 
## 
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.143506"
## 
## $RUV_ADD$lo
## [1] "0"
print(myParObjUpdated@VARIABILITY)
## $BSV_CL
## $BSV_CL$type
## [1] "var"
## 
## $BSV_CL$value
## [1] "0.000109351"
## 
## 
## $BSV_V
## $BSV_V$type
## [1] "var"
## 
## $BSV_V$value
## [1] "1e-05"
## 
## 
## $BOV_CL
## $BOV_CL$type
## [1] "var"
## 
## $BOV_CL$value
## [1] "0.14293"
## 
## 
## $BOV_V
## $BOV_V$type
## [1] "var"
## 
## $BOV_V$value
## [1] "0.179456"
## 
## 
## $BSV_KA
## $BSV_KA$value
## [1] "1.38177"
## 
## $BSV_KA$type
## [1] "var"
## 
## 
## $BSV_TLAG
## $BSV_TLAG$value
## [1] "0.01"
## 
## $BSV_TLAG$fix
## [1] "true"
## 
## $BSV_TLAG$type
## [1] "var"
## 
## 
## $BOV_COV_CL_V
## $BOV_COV_CL_V$type
## [1] "cov"
## 
## $BOV_COV_CL_V$parameter
## [1] "[eta_BOV_CL,eta_BOV_V]"
## 
## $BOV_COV_CL_V$value
## [1] "0.00575472"

Add square brackets around the correlation parameter

myParObjUpdated@VARIABILITY$BOV_COV_CL_V$value<-paste0("[",myParObjUpdated@VARIABILITY$BOV_COV_CL_V$value,"]")

Assembling the new MOG. Note that we reuse the data and model from the previous run.

myVPCMOG <- createMogObj(dataObj = getDataObjects(mdlfile)[[1]], 
        parObj = myParObjUpdated, 
        mdlObj = getModelObjects(mdlfile)[[1]], 
        taskObj = getTaskPropertiesObjects(mdlfile)[[1]])

We can then write the MOG back out to an .mdl file.

mdlfile.VPC <- paste0(uc,"_VPC.mdl")
writeMogObj(myVPCMOG,mdlfile.VPC)

Similarly as above, ddmore “VPC.PsN” function can be used to run a VPC using PsN as target tool

vpcFiles <- VPC.PsN(mdlfile.VPC,samples=20, seed=12345,
        vpcOptions ="-n_simulation=10 -auto_bin=10 -stratify_on=OCC",
        subfolder="VPC", plot=TRUE) 
## -- Fri Dec 11 14:01:30 2015
## New
## Submitted
## Job 18ea946b-4477-4e64-8b1b-eec406932ac7 progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID 18ea946b-4477-4e64-8b1b-eec406932ac7...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd03fc34957 to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase8/VPC
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Simulation
## 
## The following MESSAGEs were raised during the job execution:
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 14:02:55 2015

plot of chunk VPC

To replay the visualisation using information from the VPC SO file

pdf(paste0(uc,"_VPC.pdf"))
print(xpose.VPC(vpc.info= file.path("./VPC",vpcFiles@RawResults@DataFiles$PsN_VPC_results$path),
                vpctab= file.path("./VPC",vpcFiles@RawResults@DataFiles$PsN_VPC_vpctab$path),
                main="VPC warfarin"))
dev.off()
## rj.GD 
##     2

Simulation using simulx

Simulation with simulx is not yet possible for UseCase8

# #' The mlxR package has been developed to visualize and explore models that are encoded in
# #' MLXTRAN or PharmML 
# #' The ddmore function as.PharmML translates an MDL file (extension .mdl) to its PharmML
# #' representation. The output file (extension .xml) is saved in the working directory.
#myPharmML <- as.PharmML(mdlfile)

Use parameter values from the FOCEI estimation

#parValues <- getPopulationParameters(NM.FOCEI,what="estimates")$MLE

Simulate for the typical weight of 70. Recall that logtWT = log(WT/70).

#p <- c(parValues,logtWT=0)

Parameter values used in simulation

#print(p) 

Simulate for a dose of 100mg given at time 0 into the GUT (oral administration)

#adm <- list(target='GUT', time = 0, amount=100)

Simulate PK parameters for individuals

#ind <- list(name = c('CL','V'))

Simulate predicted (CC) and observed concentration values (Y)

#f   <- list( name = c('CC'), time = seq(0,to=50,by=1))
#y   <- list( name = c('Y'), time = c(0, 0.5, 1, 2, 3, 4, 6, 8, 12, 24, 36, 48))

Simulate 12 subjects

#g <- list( size = 12, level = 'individual',  treatment = adm)

Call simulx

#res  <- simulx(model = myPharmML,
#              parameter = p,
#              group = g,
#              output = list(ind,f,y))

Simulated parameter values for each individual

#print(res$parameter)

Plot simulated results

#plot(ggplot() + 
#               geom_line(data=res$CC, aes(x=time, y=CC, colour=id)) +
#               geom_point(data=res$Y, aes(x=time, y=Y, colour=id)) +
#               xlab("time (h)") + ylab("concentration") )
#

Simulate 1000 subjects - with simulx this is a QUICK process!

#g <- list( size = 1000, level = 'individual',  treatment = adm)

Call simulx

#res.1000  <- simulx(model =myPharmML,
#                   parameter = p,
#                   group = g,
#                   output = list(ind,f,y))

Plot of predicted concentrations band defines the percentile bands displayed level = range of values to examine (in %) 100 = full range of values number = number of bins within the level range. Plot of observed concentrations

#print(prctilemlx(res.1000$Y,band=list(number=9, level=90)))

Table of the same information

#print(prctilemlx(res.1000$Y,band=list(number=10, level=100), plot=F)$y)