model_prediction: Model predictions

Description

Function generates a data frame of model predictions for the typical value in the population, individual predictions and data predictions. The function can also be used to generate datasets without predictions using the design specified in the arguments.

Usage

model_prediction(
  poped.db = NULL,
  design = list(xt = poped.db$design[["xt"]], groupsize = poped.db$design$groupsize, m
    = poped.db$design[["m"]], x = poped.db$design[["x"]], a = poped.db$design[["a"]], ni
    = poped.db$design$ni, model_switch = poped.db$design$model_switch),
  model = list(fg_pointer = poped.db$model$fg_pointer, ff_pointer =
    poped.db$model$ff_pointer, ferror_pointer = poped.db$model$ferror_pointer),
  parameters = list(docc = poped.db$parameters$docc, d = poped.db$parameters$d, bpop =
    poped.db$parameters$bpop, covd = poped.db$parameters$covd, covdocc =
    poped.db$parameters$covdocc, sigma = poped.db$parameters$sigma),
  IPRED = FALSE,
  DV = FALSE,
  dosing = NULL,
  predictions = NULL,
  filename = NULL,
  models_to_use = "all",
  model_num_points = NULL,
  model_minxt = NULL,
  model_maxxt = NULL,
  include_sample_times = T,
  groups_to_use = "all",
  include_a = TRUE,
  include_x = TRUE,
  manipulation = NULL,
  PI = FALSE,
  PI_conf_level = 0.95
)

Value

A dataframe containing a design and (potentially) simulated data with some dense grid of samples and/or based on the input design.

Arguments

poped.db: A PopED database created by create.poped.database.
design: A list that is passed as arguments to the function create_design to create a design object.
model: A list containing the model elements to use for the predictions
parameters: A list of parameters to use in the model predictions.
IPRED: Should we simulate individual predictions?
DV: should we simulate observations?
dosing: A list of lists that adds dosing records to the data frame (Each inner list corresponding to a group in the design).
predictions: Should the resulting data frame have predictions? Either TRUE or FALSE or NULL in which case the function decides based on other arguments.
filename: A filename that the data frame should be written to in comma separate value (csv) format.
models_to_use: Which model numbers should we use? Model numbers are defined in design below using model_switch. For an explanation see create_design.
model_num_points: How many extra observation rows should be created in the data frame for each group or individual per model. If used then the points are placed evenly between model_minxt and model_maxxt. This option is used by plot_model_prediction to simulate the response of the model on a finer grid then the defined design. If NULL then only the input design is used. Can be a single value or a vector the same length as the number of models.
model_minxt: The minimum time value for extra observation rows indicated by model_num_points. A vector the same length as the number of models
model_maxxt: The minimum time value for extra observation rows indicated by model_num_points. A vector the same length as the number of models
include_sample_times: Should observations rows in the output data frame include the times indicated in the input design?
groups_to_use: Which groups should we include in the output data frame?Allowed values are "all" or a vector of numbers indicating the groups to include, e.g. c(1,3,6).
include_a: Should we include the continuous design variables in the output?
include_x: Should we include the discrete design variables in the output?
manipulation: A list of one or more expression arguments. Each expression is evaluated using the code for(i in 1:length(manipulation)){df <- within(df,{eval(manipulation[[i]])})}. Can be used to transform or create new columns in the resulting data frame. Note that these transformations are created after any model predictions occur, so transformations in columns having to do with input to model predictions will not affect the predictions.
PI: Compute prediction intervals for the data given the model. Predictions are based on first-order approximations to the model variance and a normality assumption of that variance.
PI_conf_level: The confidence level for the prediction interval computed.

Examples

Run this code

## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation 
##   for population pharmacokinetics-pharmacodynamics studies", 
##   Br. J. Clin. Pharm., 2014. 

library(PopED)

## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.CL

## -- parameter definition function 
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
  parameters=c(CL=bpop[1]*exp(b[1]),
               V=bpop[2]*exp(b[2]),
               KA=bpop[3]*exp(b[3]),
               Favail=bpop[4],
               DOSE=a[1])
    return(parameters) 
}

## -- Define initial design  and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
                                  fg_fun=sfg,
                                  fError_fun=feps.prop,
                                  bpop=c(CL=0.15, V=8, KA=1.0, Favail=1), 
                                  notfixed_bpop=c(1,1,1,0),
                                  d=c(CL=0.07, V=0.02, KA=0.6), 
                                  sigma=0.01,
                                  groupsize=32,
                                  xt=c( 0.5,1,2,6,24,36,72,120),
                                  minxt=0,
                                  maxxt=120,
                                  a=70)

## data frame with model predictions
df_1 <- model_prediction(poped.db)
head(df_1,n=20)

##  data frame with variability 
df_2 <- model_prediction(poped.db,DV=TRUE)
head(df_2,n=20)

## data frame with variability (only IPRED, no DV)
df_3 <- model_prediction(poped.db,IPRED=TRUE)
head(df_3,n=20)

## data frame with model predictions, no continuous design variables in data frame
df_4 <- model_prediction(poped.db,include_a = FALSE)
head(df_4,n=20)

## -- 2 groups
poped.db.2 <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
                                    fg_fun=sfg,
                                    fError_fun=feps.prop,
                                    bpop=c(CL=0.15, V=8, KA=1.0, Favail=1), 
                                    notfixed_bpop=c(1,1,1,0),
                                    d=c(CL=0.07, V=0.02, KA=0.6), 
                                    sigma=0.01,
                                    groupsize=rbind(3,3),
                                    m=2,
                                    xt=c( 0.5,1,2,6,24,36,72,120),
                                    minxt=0,
                                    maxxt=120,
                                    a=rbind(70,50))

df_5 <- model_prediction(poped.db.2,DV=TRUE)
head(df_5,n=20)

## without a poped database, just describing the design
## Useful for creating datasets for use in other software (like NONMEM)
design_1 <- list(
  xt=c( 0.5,1,2,6,24,36,72,120),
  m=2,
  groupsize=3)

design_2 <- list(
  xt=c( 0.5,1,2,6,24,36,72,120),
  m=2,
  groupsize=3,
  a=c(WT=70,AGE=50))

design_3 <- list(
  xt=c( 0.5,1,2,6,24,36,72,120),
  m=2,
  groupsize=3,
  a=list(c(WT=70,AGE=50),c(AGE=45,WT=60)))

(df_6 <- model_prediction(design=design_1))
(df_7 <- model_prediction(design=design_2))
(df_8 <- model_prediction(design=design_3))
(df_9 <- model_prediction(design=design_3,DV=TRUE))

# generate random deviations in WT for each individual
df_10 <- model_prediction(design=design_3,DV=TRUE,
                          manipulation=expression({for(id in unique(ID)) 
                              WT[ID==id] = rnorm(1,WT[ID==id],WT[ID==id]*0.1);id <- NULL}))
head(df_10,n=20)

# generate random deviations in WT and AGE for each individual
df_11 <- model_prediction(design=design_3,DV=TRUE,
                          manipulation=list(
                            expression(for(id in unique(ID)) 
                              WT[ID==id] = rnorm(1,WT[ID==id],WT[ID==id]*0.1)),
                            expression(for(id in unique(ID)) 
                              AGE[ID==id] = rnorm(1,AGE[ID==id],AGE[ID==id]*0.2)),
                            expression(id <- NULL)
                          ))
head(df_10,n=20)

## create dosing rows 
dosing_1 <- list(list(AMT=1000,RATE=NA,Time=0.5),list(AMT=3000,RATE=NA,Time=0.5))
dosing_2 <- list(list(AMT=1000,RATE=NA,Time=0.5))
dosing_3 <- list(list(AMT=1000,Time=0.5))
dosing_4 <- list(list(AMT=c(1000,20),Time=c(0.5,10))) # multiple dosing


(df_12 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_1))
(df_13 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_2))
(df_14 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_3))
(df_15 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_4))


model_prediction(design=design_3,DV=TRUE,dosing=dosing_4,model_num_points = 10)
model_prediction(design=design_3,DV=TRUE,dosing=dosing_4,model_num_points = 10,model_minxt=20)

design_4 <- list(
  xt=c( 0.5,1,2,6,24,36,72,120),
  model_switch=c(1,1,1,1,2,2,2,2),
  m=2,
  groupsize=3,
  a=list(c(WT=70,AGE=50),c(AGE=45,WT=60)))

model_prediction(design=design_4,DV=TRUE,dosing=dosing_4)
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = 10)
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = 10,
                 model_minxt=10,model_maxxt=100)
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = 10,
                 model_minxt=c(20,20),model_maxxt=c(100,100))
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = c(10,10),
                 model_minxt=c(20,20),model_maxxt=c(100,100))

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