1 reply to this topic

[linda]

Hi Everyone,

 

I am working on a project about determine sample size of a trial design. I need to keep enrolling different patients dataset and do pop PK fitting until criterion is fulfilled. Should I use RsNLME or Pirana? Could RsNLME can execute NLME automatically with a for loop? If yes, where could I get a case study or tutorial for RsNLME?

 

Thanks,

Linda 

[cradhakr]

Hi Linda,

 

Yes, You can set up a loop for executing the model with RsNLME. However, this has to be done using R as we do not have this enabled through. We do not have a specific exaple to suite your need.

The below example was used for simulation using loop function. You could tweak the code accordingly to your needs. Hope this is useful to you.

 

 

for(i in 1:3){

 

  #i<-1

 

  # 2. Import Dataset ----

  # (we can modify this to set up simulation conditions)

  simdata<-read.csv(paste0(i,"/Data_testCertara.csv"))

 

 

  # 4. Create Model from PML and dataset using "textualmodel" function ----

  model <- textualmodel(modelName = paste(i),

                        data =  simdata,

                        mdl = paste0(i,"/model_testCertara.mdl"))

 

  # * 4.1 map model variables to data columns ----

 

  model <- colMapping(model, mappings = c(id = "X..id", A2S = "amt"))

 

 

  # 5. Fit Model ----

 

  # * 5.1 Define desired additional output table(s) ----

  TableCObs <- NlmeSimTableDef(name = "TableCObs.csv",

                                  timesList = seq(0,1000,10),

                                  variablesList = c("A2S","A3S","A4S","A5S","A6S"))

 

  TableParm <- NlmeSimTableDef(name = "TableParm.csv",

                                  timesList = c(0,0.0001,1,10,100,1000),

                                  variablesList = c("tvPappS", "tvCL3tot", "tvCL4tot",

                                                    "tvLIN_PappS","tvLIN_CL3tot","tvLIN_CL4tot",

                                                    "V2","V2_evs", "V4", "V4_evs", "Sample_VA",

                                                    "Sample_Vb", "tkev_a", "tkev"))

 

  # * 5.2 Fit setup ----

 

  # * * 5.2.1 Engine Settings (see: ?engineParams) ----

 

  EngineSettings <- engineParams(model = model,

                                 method = "Naive-Pooled",

                                 ODE = "Stiff")

 

 

 

  # * 5.3 Execute Fit ----

 

  modelfit <- fitmodel(model = model,

                     params = EngineSettings)

 

 

}

 

Thnaks

Mouli