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 with the cursor 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=FALSE))
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)

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Export results to PDF file

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

Model Development

ESTIMATE model parameters using Monolix

mlx <- estimate(mdlfile, target="MONOLIX", subfolder="Monolix")
## -- Tue Aug 16 15:03:59 2016
## New
## Submitted
## Job de2733a5-f8ef-4cdf-bd02-191afe25f3c3 progress:
## Running [ ............... ]
## Importing Results
## Copying the result data back to the local machine for job ID de2733a5-f8ef-4cdf-bd02-191afe25f3c3...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356424da32da to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase8/Monolix
## Done.
## 
## 
## The following main elements were parsed successfully:
##   ToolSettings
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::PrecisionPopulationEstimates::MLE
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::IndividualContribToLL
##   Estimation::OFMeasures::InformationCriteria
##   Estimation::OFMeasures::LogLikelihood
## 
## Completed
## -- Tue Aug 16 15:09:06 2016
slotNames(mlx)
## [1] "ToolSettings"     "RawResults"       "TaskInformation" 
## [4] "Estimation"       "ModelDiagnostic"  "Simulation"      
## [7] "OptimalDesign"    ".pathToSourceXML"

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)
## $MLE
##        POP_V    BETA_V_WT       POP_KA       POP_CL   BETA_CL_WT 
##      8.59584      1.00000      7.48435      0.10833      0.75000 
##     POP_TLAG        BSV_V       BSV_KA       BSV_CL     BSV_TLAG 
##      1.12123      0.00005      2.06309      0.00050      0.01000 
## COV_BSV_CL_V        BOV_V       BOV_CL      RUV_ADD     RUV_PROP 
##     -0.19056      0.16628      0.17703      0.31489      0.08215
print(getPopulationParameters(mlx, what="precisions"))
## $MLE
##       Parameter      MLE        SE      RSE
## 1    BETA_CL_WT  0.75000   0.00000     0.00
## 2     BETA_V_WT  1.00000   0.00000     0.00
## 3        BOV_CL  0.17703   0.05113    28.88
## 4         BOV_V  0.16628   0.04804    28.89
## 5        BSV_CL  0.00050   0.03666  7377.59
## 6        BSV_KA  2.06309   0.06872     3.33
## 7      BSV_TLAG  0.01000   0.00000     0.00
## 8         BSV_V  0.00005   0.03394 72148.31
## 9  COV_BSV_CL_V -0.19056 134.11375 70377.34
## 10       POP_CL  0.10833   0.00641     5.92
## 11       POP_KA  7.48435   2.98977    39.95
## 12     POP_TLAG  1.12123   0.03814     3.40
## 13        POP_V  8.59584   0.48671     5.66
## 14      RUV_ADD  0.31489   0.04375    13.89
## 15     RUV_PROP  0.08215   0.00800     9.74
print(getEstimationInfo(mlx))
## $OFMeasures
## $OFMeasures$LogLikelihood
## $OFMeasures$LogLikelihood[[1]]
## [1] -800.295
## 
## 
## $OFMeasures$IndividualContribToLL
##    Subject ICtoLL
## 1        1 -44.99
## 2        2 -17.20
## 3        3 -37.08
## 4        4 -46.24
## 5        5 -27.14
## 6        6 -26.98
## 7        7 -31.72
## 8        8 -33.04
## 9        9 -55.05
## 10      10 -28.10
## 11      12 -30.02
## 12      13 -45.01
## 13      14 -59.88
## 14      15 -45.74
## 15      16 -27.04
## 16      17 -20.45
## 17      18 -18.17
## 18      19 -17.68
## 19      20 -19.15
## 20      22 -16.87
## 21      23 -16.26
## 22      24 -21.42
## 23      25 -19.98
## 24      26 -20.26
## 25      28 -19.96
## 26      30 -16.64
## 27      32 -19.05
## 28      33 -19.18
## 
## $OFMeasures$InformationCriteria
## $OFMeasures$InformationCriteria$AIC
## [1] 1624.59
## 
## $OFMeasures$InformationCriteria$BIC
## [1] 1648.9
## 
## 
## 
## $Messages
## list()

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)
## 
## Removed dose rows in rawData slot of SO to enable merge with Predictions data.
## 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(mlx.xpdb))

plot of chunk unnamed-chunk-18

print(ind.plots(mlx.xpdb))

plot of chunk unnamed-chunk-18 plot of chunk unnamed-chunk-18

Export results to PDF file

pdf("GOF_MLX.pdf")
print(basic.gof(mlx.xpdb))
print(ind.plots(mlx.xpdb))
dev.off()
## png 
##   2

SAEM Estimation with NONMEM

By default, a covariance step is not run when estimating in NONMEM. To see how it can be requested, see UseCase1_1.mdl

NM <- estimate(mdlfile, target="NONMEM", subfolder="NONMEM")
## -- Tue Aug 16 15:09:27 2016
## New
## Submitted
## Job 7c11b510-a3b5-46f6-acbc-fa4a3895e34b progress:
## Running [ ....................... ]
## Importing Results
## Copying the result data back to the local machine for job ID 7c11b510-a3b5-46f6-acbc-fa4a3895e34b...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35646a581c4 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase8/NONMEM
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::Deviance
## 
## The following WARNINGs were raised during the job execution:
##  Name change: Parameter label "OMEGA(6,6)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_6_6_
##  Name change: Parameter label "OMEGA(8,8)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_8_8_
## 
## The following MESSAGEs were raised during the job execution:
##  estimation_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 4.5.27
## 
## Completed
## -- Tue Aug 16 15:17:13 2016

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     POP_TLAG     RUV_PROP 
##   0.10749700   8.41037000   1.73247000   0.87796800   0.09654270 
##      RUV_ADD   BETA_CL_WT    BETA_V_WT       BSV_CL COV_BSV_CL_V 
##   0.31577000   0.75000000   1.00000000   0.01713800   0.01112120 
##        BSV_V       BSV_KA     BSV_TLAG       BOV_CL   OMEGA_6_6_ 
##   0.00721707   0.09996940   0.01000000   0.14436300   0.14436300 
##        BOV_V   OMEGA_8_8_ 
##   0.18440600   0.18440600
print(parameters_mlx)
## $MLE
##        POP_V    BETA_V_WT       POP_KA       POP_CL   BETA_CL_WT 
##      8.59584      1.00000      7.48435      0.10833      0.75000 
##     POP_TLAG        BSV_V       BSV_KA       BSV_CL     BSV_TLAG 
##      1.12123      0.00005      2.06309      0.00050      0.01000 
## COV_BSV_CL_V        BOV_V       BOV_CL      RUV_ADD     RUV_PROP 
##     -0.19056      0.16628      0.17703      0.31489      0.08215
print(getEstimationInfo(NM))
## $OFMeasures
## $OFMeasures$Deviance
## $OFMeasures$Deviance[[1]]
## [1] -375.6158
## 
## 
## 
## $Messages
## $Messages$Info
## $Messages$Info$estimation_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 4.5.27"
## 
## 
## $Messages$Warnings
## $Messages$Warnings$`Name change`
## [1] "Parameter label \"OMEGA(8,8)\" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_8_8_"

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-22

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

plot of chunk unnamed-chunk-22

print(ind.plots(nm.xpdb))

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print(parm.hist(nm.xpdb))

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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()
## png 
##   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.

By default, a covariance step is not run when estimating in PsN. To see how it can be requested, see UseCase1_1.mdl

NM.FOCEI <- estimate(mdlfile.FOCEI, target="PsN", subfolder="NONMEM_FOCEI")
## -- Tue Aug 16 15:17:40 2016
## New
## Submitted
## Job b72ea0c8-a430-4c3a-8e2a-ea3d121e4d74 progress:
## Running [ ....... ]
## Importing Results
## Copying the result data back to the local machine for job ID b72ea0c8-a430-4c3a-8e2a-ea3d121e4d74...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356475594ddf to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase8/NONMEM_FOCEI
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::Deviance
## 
## The following WARNINGs were raised during the job execution:
##  estimation_successful: 0
##  rounding_errors: 1
##  hessian_reset: 6
##  zero_gradients: 16
##  final_zero_gradients: 1
##  estimate_near_boundary: 1
##  Name change: Parameter label "OMEGA(6,6)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_6_6_
##  Name change: Parameter label "OMEGA(8,8)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_8_8_
## 
## 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 4.5.27
## 
## Completed
## -- Tue Aug 16 15:20:03 2016

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     POP_TLAG     RUV_PROP 
##   0.10765400   8.49874000   1.60377000   0.86818700   0.09694860 
##      RUV_ADD   BETA_CL_WT    BETA_V_WT       BSV_CL COV_BSV_CL_V 
##   0.31600700   0.75000000   1.00000000   0.01161790   0.00573471 
##        BSV_V       BSV_KA     BSV_TLAG       BOV_CL   OMEGA_6_6_ 
##   0.00284061   0.08430700   0.01000000   0.13599200   0.13599200 
##        BOV_V   OMEGA_8_8_ 
##   0.18079900   0.18079900
print(parameters_nm)
## $MLE
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##   0.10749700   8.41037000   1.73247000   0.87796800   0.09654270 
##      RUV_ADD   BETA_CL_WT    BETA_V_WT       BSV_CL COV_BSV_CL_V 
##   0.31577000   0.75000000   1.00000000   0.01713800   0.01112120 
##        BSV_V       BSV_KA     BSV_TLAG       BOV_CL   OMEGA_6_6_ 
##   0.00721707   0.09996940   0.01000000   0.14436300   0.14436300 
##        BOV_V   OMEGA_8_8_ 
##   0.18440600   0.18440600
print(parameters_mlx)
## $MLE
##        POP_V    BETA_V_WT       POP_KA       POP_CL   BETA_CL_WT 
##      8.59584      1.00000      7.48435      0.10833      0.75000 
##     POP_TLAG        BSV_V       BSV_KA       BSV_CL     BSV_TLAG 
##      1.12123      0.00005      2.06309      0.00050      0.01000 
## COV_BSV_CL_V        BOV_V       BOV_CL      RUV_ADD     RUV_PROP 
##     -0.19056      0.16628      0.17703      0.31489      0.08215

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-28

Export graphs to a PDF file

pdf("GOF_NM_FOCEI.pdf")
print(basic.gof(nmfocei.xpdb))
dev.off()
## png 
##   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)
## -- Tue Aug 16 15:20:10 2016
## New
## Submitted
## Job 930426a6-fe4a-4bde-9feb-e8bb0ad51fb5 progress:
## Running [ ..................................... ]
## Importing Results
## Copying the result data back to the local machine for job ID 930426a6-fe4a-4bde-9feb-e8bb0ad51fb5...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356479275ef to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase8/Bootstrap
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::PopulationEstimates::OtherMethodBootstrap
##   Estimation::PrecisionPopulationEstimates::OtherMethodBootstrap
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::Deviance
## 
## The following WARNINGs were raised during the job execution:
##  estimation_successful: 0
##  rounding_errors: 1
##  hessian_reset: 6
##  zero_gradients: 16
##  final_zero_gradients: 1
##  estimate_near_boundary: 1
##  Name change: Parameter label "OMEGA(6,6)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_6_6_
##  Name change: Parameter label "OMEGA(8,8)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_8_8_
## 
## 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 4.5.27
## 
## Completed
## -- Tue Aug 16 15:32:34 2016
## Warning: NAs introduced by coercion
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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")))
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## NULL
## 
## [[5]]
## NULL
dev.off()
## png 
##   2

Extract parameter estimates and precision from bootstrap results.

print(getPopulationParameters(bootstrapResults, what="estimates"))
## $MLE
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##   0.10765400   8.49874000   1.60377000   0.86818700   0.09694860 
##      RUV_ADD   BETA_CL_WT    BETA_V_WT       BSV_CL COV_BSV_CL_V 
##   0.31600700   0.75000000   1.00000000   0.01161790   0.00573471 
##        BSV_V       BSV_KA     BSV_TLAG       BOV_CL   OMEGA_6_6_ 
##   0.00284061   0.08430700   0.01000000   0.13599200   0.13599200 
##        BOV_V   OMEGA_8_8_ 
##   0.18079900   0.18079900 
## 
## $Bootstrap
##                 Parameter        Mean      Median
## BETA_CL_WT     BETA_CL_WT 0.750000000 0.750000000
## BETA_V_WT       BETA_V_WT 1.000000000 1.000000000
## BOV_CL             BOV_CL 0.125021900 0.125004000
## BOV_V               BOV_V 0.167257000 0.176528500
## BSV_CL             BSV_CL 0.020738130 0.013904600
## BSV_KA             BSV_KA 0.051870250 0.072451550
## BSV_TLAG         BSV_TLAG 0.010000000 0.010000000
## BSV_V               BSV_V 0.004629412 0.002615225
## COV_BSV_CL_V COV_BSV_CL_V 0.004299062 0.002215090
## OMEGA_6_6_     OMEGA_6_6_ 0.125021900 0.125004000
## OMEGA_8_8_     OMEGA_8_8_ 0.167257000 0.176528500
## POP_CL             POP_CL 0.107126600 0.107725000
## POP_KA             POP_KA 1.566207000 1.544185000
## POP_TLAG         POP_TLAG 0.824302300 0.841148500
## POP_V               POP_V 8.291708000 8.305800000
## RUV_ADD           RUV_ADD 0.362512000 0.357364000
## RUV_PROP         RUV_PROP 0.094574080 0.092000100

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 0.750000000  7.500000e-01 0.75000000
## 2     BETA_V_WT 1.000000000 1.000000000  1.000000e+00 1.00000000
## 3        BOV_CL 0.125021900 0.125004000  8.304487e-02 0.17199000
## 4         BOV_V 0.167257000 0.176528500  8.167527e-02 0.21465980
## 5        BSV_CL 0.020738130 0.013904600  1.000000e-05 0.06753287
## 6        BSV_KA 0.051870250 0.072451550  1.000000e-05 0.13108320
## 7      BSV_TLAG 0.010000000 0.010000000  1.000000e-02 0.01000000
## 8         BSV_V 0.004629412 0.002615225  9.931232e-06 0.01932321
## 9  COV_BSV_CL_V 0.004299062 0.002215090 -7.531800e-03 0.01899782
## 10   OMEGA_6_6_ 0.125021900 0.125004000  8.304487e-02 0.17199000
## 11   OMEGA_8_8_ 0.167257000 0.176528500  8.167527e-02 0.21465980
## 12       POP_CL 0.107126600 0.107725000  9.175291e-02 0.11957350
## 13       POP_KA 1.566207000 1.544185000  1.217220e+00 1.90200400
## 14     POP_TLAG 0.824302300 0.841148500  5.077280e-01 0.99684000
## 15        POP_V 8.291708000 8.305800000  7.421821e+00 9.22034800
## 16      RUV_ADD 0.362512000 0.357364000  1.182200e-01 0.57709280
## 17     RUV_PROP 0.094574080 0.092000100  6.085998e-02 0.13466380

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, a between occassion variability parameter is recorded for each OCC when MDL only specifies one. 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)
variabilityPar <- variabilityPar[variabilityNames]
variabilityPar
##       BSV_CL        BSV_V COV_BSV_CL_V       BOV_CL        BOV_V 
##   0.01161790   0.00284061   0.00573471   0.13599200   0.18079900 
##       BSV_KA     BSV_TLAG     RUV_PROP      RUV_ADD 
##   0.08430700   0.01000000   0.09694860   0.31600700

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] "0.107654"
## 
## $POP_CL$lo
## [1] "0.001"
## 
## 
## $POP_V
## $POP_V$value
## [1] "8.49874"
## 
## $POP_V$lo
## [1] "0.001"
## 
## 
## $POP_KA
## $POP_KA$value
## [1] "1.60377"
## 
## $POP_KA$lo
## [1] "0.001"
## 
## 
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.868187"
## 
## $POP_TLAG$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"
print(myParObjUpdated@VARIABILITY)
## $BSV_CL
## $BSV_CL$value
## [1] "0.0116179"
## 
## 
## $BSV_V
## $BSV_V$value
## [1] "0.00284061"
## 
## 
## $COV_BSV_CL_V
## $COV_BSV_CL_V$value
## [1] "0.00573471"
## 
## 
## $BOV_CL
## $BOV_CL$value
## [1] "0.135992"
## 
## 
## $BOV_V
## $BOV_V$value
## [1] "0.180799"
## 
## 
## $BSV_KA
## $BSV_KA$value
## [1] "0.084307"
## 
## 
## $BSV_TLAG
## $BSV_TLAG$value
## [1] "0.01"
## 
## $BSV_TLAG$fix
## [1] "true"
## 
## 
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.0969486"
## 
## $RUV_PROP$lo
## [1] "0"
## 
## 
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.316007"
## 
## $RUV_ADD$lo
## [1] "0"

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) 
## -- Tue Aug 16 15:32:48 2016
## New
## Submitted
## Job 4cb7c83f-634c-44e5-ab0f-ffefd71d5542 progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID 4cb7c83f-634c-44e5-ab0f-ffefd71d5542...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job3564279b5bba to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase8/VPC
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   SimulationSimulationBlock
##   SimulationSimulationBlock
## 
## The following WARNINGs were raised during the job execution:
##  Name change: Parameter label "OMEGA(6,6)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_6_6_
##  Name change: Parameter label "OMEGA(8,8)" not specified or not a legal symbolIdType. Setting/changing it to: OMEGA_8_8_
## 
## The following MESSAGEs were raised during the job execution:
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Tue Aug 16 15:34:11 2016

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()
## png 
##   2

Simulation using simulx

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,WT=70)

Parameter values used in simulation

print(p) 
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##   0.10765400   8.49874000   1.60377000   0.86818700   0.09694860 
##      RUV_ADD   BETA_CL_WT    BETA_V_WT       BSV_CL COV_BSV_CL_V 
##   0.31600700   0.75000000   1.00000000   0.01161790   0.00573471 
##        BSV_V       BSV_KA     BSV_TLAG       BOV_CL   OMEGA_6_6_ 
##   0.00284061   0.08430700   0.01000000   0.13599200   0.13599200 
##        BOV_V   OMEGA_8_8_           WT 
##   0.18079900   0.18079900  70.00000000

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

adm <- list(type=1, 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))

Define a varlevel (ID, OCC)

vl <- list(time=c(0,20), name = 'occ')

Simulate 12 subjects

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

Call simulx

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

Simulated parameter values for each individual

print(res$parameter)
##    id time occ         CL         V
## 1   1    0   1 0.10244221  7.596627
## 2   1   20   2 0.10542580 10.350770
## 3   2    0   1 0.12017490  7.253387
## 4   2   20   2 0.12989547 20.485263
## 5   3    0   1 0.10032233  7.000948
## 6   3   20   2 0.10719447  4.855010
## 7   4    0   1 0.06975548  6.032570
## 8   4   20   2 0.17417837  8.962542
## 9   5    0   1 0.09826270  6.383978
## 10  5   20   2 0.08530869 18.833919
## 11  6    0   1 0.08139053  8.875907
## 12  6   20   2 0.22647883  5.122665
## 13  7    0   1 0.09996817  5.561151
## 14  7   20   2 0.09092070  3.658492
## 15  8    0   1 0.12048452 15.258213
## 16  8   20   2 0.06167403  6.596337
## 17  9    0   1 0.08841777 14.990048
## 18  9   20   2 0.11606069  7.029747
## 19 10    0   1 0.17552216  6.469904
## 20 10   20   2 0.16037116  8.896697
## 21 11    0   1 0.17292700 13.041433
## 22 11   20   2 0.18204104 18.545997
## 23 12    0   1 0.05089589  4.600040
## 24 12   20   2 0.09578542  9.445735

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") )

plot of chunk unnamed-chunk-49

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,
                    varlevel = vl,
                    parameter = p,
                    group = g,
                    output = list(ind,f,y))

Plot prediction intervals with prctilemlx. band defines the percentile bands displayed:

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

plot of chunk unnamed-chunk-51

Table of the same information

print(prctilemlx(res.1000$Y,band=list(number=10, level=100), plot=FALSE)$y)
##    time         0%        10%        20%         30%          40%
## 1   0.0 -0.6675970 -0.3893490 -0.2770639 -0.20204216 -0.076862378
## 2   0.5 -0.7171826 -0.3441493 -0.1969014 -0.09217686 -0.001038873
## 3   1.0 -0.4845512  0.3292093  0.6690394  0.92511222  1.186947741
## 4   2.0  3.2453886  5.0569583  6.2181194  7.22560506  7.919226614
## 5   3.0  4.3105118  5.8143015  7.1276934  8.06603820  8.675571085
## 6   4.0  3.5282202  6.5380003  7.6958544  8.43404829  9.250557498
## 7   6.0  3.6636547  6.1142584  7.1151334  8.56539332  9.526902067
## 8   8.0  3.0396481  5.7593859  6.9853183  8.03557329  9.109451886
## 9  12.0  3.4134747  5.9532848  6.8558701  8.21395952  9.069037316
## 10 24.0  2.4485005  4.8335180  5.6900931  6.28071585  7.135451470
## 11 36.0  1.6224182  4.0266771  4.7126886  5.73054560  6.310645792
## 12 48.0  1.1921365  2.6650093  3.5743182  4.60443760  5.479547526
##            50%         50%         60%        70%        80%        90%
## 1  -0.03819879 -0.03819879  0.04787303  0.1042791  0.1875122  0.3940907
## 2   0.04616350  0.04616350  0.08673767  0.1745893  0.2834916  0.3827955
## 3   1.67025051  1.67025051  1.92045266  2.5731564  3.1121236  3.8247268
## 4   8.88545951  8.88545951 10.07577343 11.9564795 13.0355463 15.6260955
## 5  10.68692744 10.68692744 12.32518386 13.3944069 14.9967849 19.8530378
## 6  10.84837431 10.84837431 11.99109645 13.4530370 15.6604290 19.7125022
## 7  10.77033328 10.77033328 12.24275198 13.2984038 14.9832177 19.1532520
## 8   9.77051534  9.77051534 11.21094337 12.4230094 14.5679518 18.8604898
## 9  10.05997184 10.05997184 10.89377747 12.0101220 13.5822036 17.1077248
## 10  7.92299038  7.92299038  8.91152009 10.4600606 12.5890867 15.7225529
## 11  7.10917068  7.10917068  7.70551864  8.8801954 10.9846403 13.9558678
## 12  6.32913599  6.32913599  7.23652849  8.7438275 10.0544032 12.6511396
##          100%
## 1   0.8585940
## 2   0.7834229
## 3   9.6110400
## 4  26.9686242
## 5  37.1569185
## 6  42.0421803
## 7  28.4475086
## 8  26.9473581
## 9  24.0993872
## 10 29.6514373
## 11 25.2577511
## 12 18.3750044